Image Degradation Analysis
| Image Degradation Analysis | |
|---|---|
| Abbreviation | IDA |
| First Introduced | as Photoelectromagnetic (PEM) Image Analysis |
| Year Introduced | 2021 |
| Type | Forensic Image Analysis Method |
| Developer | Andrew Lehti |
| Primary focus | Compression differences, lighting patterns, color transitions and image boundaries |
| Standard method | JPEG quality 5, RGB difference mapping and amplification |
| Protocol | IDA55 |
| Published | 2026 white paper |
| DOI | 10.6084/m9.figshare.31323694 |
| Case study | Apollo Mission Photographs |
Image Degradation Analysis (IDA) is a method of examining photographs by making small changes in their colors and brightness much easier to see. Developed by Andrew Lehti, the process deliberately degrades a copy of a photograph, compares it with the original, and amplifies the differences into a detailed visual map.
A conventional photograph shows a scene as the camera recorded and processed it. An IDA image shows something different: which parts of the photograph respond to controlled degradation, and how those changes are distributed across the picture. Smooth-looking areas can become bands of color; bright regions may reveal rings and gradients; boundaries that were difficult to distinguish can become prominent.
IDA is especially useful for comparing illumination, color transitions, and repeated image structures. It gives investigators, photographers and interested readers an additional way to explore ordinary photographs, archival material, studio images and digitally constructed scenes.
The method was originally called Photoelectromagnetic (PEM) Image Analysis. Its present name describes the actual procedure more plainly. Lehti introduced the approach in Image Degradation Analysis (IDA): Forensic Applications for Light Physics, a 2026 white paper containing the methodology and extensive photographic comparisons (read the white paper).
Figure 23 demonstrates comparison and the purpose of the method. The first (labeled: original) preserves the scene's familiar appearance, while the processed versions emphasize changes that are difficult to perceive directly, showing a sister forensic method, ELA, in the second image (labeled: ELA) and the IDA method in the third image (labeled: IDA). IDA turns those changes into an image that can be inspected, compared, and reproduced across mediums, light sources, light types, light intensities, and the very fingerprints of light.
Examining an Image Through Degradation
Most photographs are designed to look continuous. Light passes across objects, colors blend, and light reflects differently across different materials and different colors of the same materials. This makes a photograph easy to recognize, but it can make some of its underlying color relationships difficult to study.
IDA approaches the same photograph from the opposite direction. Instead of improving or sharpening it, the process intentionally removes information and studies where the greatest changes occur. Those changes are then made conspicuous through amplification.
The resulting map can help an examiner:
- Locate concentrated regions of brightness and examine their shape.
- Follow color and brightness transitions across a surface.
- Compare patterns produced under sunlight, studio lamps and other lighting arrangements.
- Identify repeated bands, grids, outlines and distinct image boundaries.
- Compare different versions of a photograph or multiple photographs from one collection.
- Document the same visual observations using a repeatable procedure.
The original photograph remains the reference throughout. IDA is an additional view of that photograph, not a replacement for it.
How IDA Works: Degrade, Compare, Reveal
The central procedure has three stages. They can be performed in a photo editor or implemented with code.
1. Degrade a Duplicate
The examiner begins with an unchanged original and saves a separate copy as a JPEG at a very low quality setting. In the standard procedure, that setting is 5 out of 100. If it does not save as a quality=5, then it is not standard IDA, it is a deviation of the method which should be noted.
JPEG normally makes photographic files smaller by simplifying image information. At quality 5, that simplification becomes especially strong. Fine detail changes, neighboring color values are represented differently, and some transitions become less precise. This deliberate alteration forms the basis of IDA and has produced remarkably consistent results across tens of thousands of photographs examined by the author. Independent researchers and interested individuals are encouraged to apply the method to their own image collections and compare their findings. Future development will also focus on establishing a standardized procedure for analyzing heavily edited or previously manipulated images, ensuring greater consistency and comparability across different image sources.
The important point in this step is that both versions depict the same scene and occupy the same pixel positions. The differences arise from the chosen degradation step, allowing a direct comparison.
2. Extract the Differences
The low-quality JPEG is reopened and compared with the original, one pixel at a time. For every location, the process measures how much the red, green and blue values changed. In ordinary editing software, a Difference layer produces this comparison.
Unchanged pixel values become black. Values that changed remain visible as small differences, although the complete map initially tends to look extremely dark.
An almost black difference map does not mean that nothing was found. It means that the changes are relatively small on the normal display scale, and is expected at this step. IDA's final stage is designed to make these small values visible.
3. Amplify the Result
The extracted differences are increased in intensity and their tones adjusted. This turns faint variations into clearly visible patterns of color, shape and contrast.
The Pluto example provides a clear illustration of how the process works. Each panel serves a specific purpose: preserving the original image, introducing controlled changes, isolating those changes, and making them easier to examine. The final map is produced entirely through image analysis, without adding non-original data, painting, filters or effects, or marking the photograph. In this example, a high-resolution image of Pluto was used to identify the boundaries where smaller images had been stitched together or edited to create the final composite photograph.
What an IDA Map Shows
An Image Degradation Analysis (IDA) map reveals differences in a photograph that are often too subtle to recognize by looking at the original image alone. Although its vivid colors, rings, outlines, and unusual patterns may initially seem confusing, they serve a practical purpose: making differences in the image's recorded brightness and color easier to see and examine.
Every photograph contains information about how light interacted with the scene. Sunlight, artificial lighting, shadows, reflections, and the surfaces of objects all influence the brightness and color values recorded by the camera. IDA examines this recorded information by deliberately compressing a copy of the photograph and comparing it with the original. The mathematical differences are isolated and amplified, producing a map that highlights how different areas of the photograph respond to the same controlled degradation process.
The resulting patterns are not manually drawn onto the photograph. They are generated from differences between the original image and its degraded copy. Several characteristics are particularly useful when examining an IDA map, including brightness gradients, concentrated areas of light, repeating patterns, and unexpected boundaries between neighboring regions.
Brightness Gradients
A brightness gradient is a gradual transition from light to dark, such as sunlight fading into shadow or the illuminated area surrounding a lamp. These changes are often difficult to distinguish in ordinary photographs because they blend with surface textures, colors, and reflections. IDA makes these transitions more visible by highlighting differences in the recorded image. Gradients may appear as colorful bands, outlines, or contour-like patterns that help trace changes in brightness, material, source, refraction, and reflection across a scene.
Example: Sunlight Across a Street
Figure 7 compares an ordinary street photograph with its IDA map. In the original, sunlight illuminates the pavement while people and surrounding objects cast shadows. After processing, the differences between illuminated and shaded areas become more prominent, revealing distinct color transitions across the scene.
These patterns can be compared directly with the original photograph to examine how recorded brightness changes across different surfaces. They illustrate how IDA can make subtle variations easier to observe without manually adding information to the image.
However, the presence of a particular pattern does not always indicate artificial lighting or manipulation. Natural illumination, reflections, surface properties, and light sources, or in rare cases of heavy color manipulation, can all influence the result. Comparing these patterns with the original scene provides a useful foundation for interpreting more complex IDA features, including hotspots and repeating geometric structures.
Color-channel Differences
A color photograph is made up of three primary color channels: red, green, and blue (RGB). Together, they produce the millions of colors visible in an image. When a photograph undergoes JPEG degradation, these channels may be affected differently. IDA isolates and amplifies those differences, making them visible as vivid bands of cyan, magenta, yellow, green, and other colors.
These rainbow-like patterns are not simply decorative effects. They represent measurable differences in how the recorded color information responds to degradation. Their shapes, positions, and relationships can help reveal subtle variations that are difficult to recognize in the original photograph.
Figure 22 demonstrates this effect using an image of the Sun. The processed image reveals several layers of colored contours surrounding its bright center. Examining their shape, spacing, and distribution provides another way to study how light and color were recorded in the photograph.
Contours, Rings and Boundaries
IDA maps frequently reveal rings, outlines, and distinct boundaries that are barely noticeable, or entirely hidden, in the original photograph. Some appear as evenly spaced circles surrounding a bright area, while others form irregular, branching patterns that extend across the scene.
These structures help illustrate where recorded brightness and color values change. For example, a concentrated light source may produce rings surrounding its brightest area, while shadows, reflections, or differences between neighboring surfaces may create more irregular boundaries. It allows for forensic scientists to determine angles, density, and distance of light sources.
The important distinction becomes: where these patterns appear, and how it is shaped, and how it relates to the original and comparable photographs. Comparing these characteristics across different images can help identify similarities, differences, and unusual features that warrant closer examination.
Understanding Concentrated Light: Hotspots
A hotspot is an area where light is concentrated more strongly than in its surroundings. In a photograph, it may appear as a bright circle, an oval, or a focused area of illumination that gradually fades toward the edges. Hotspots are commonly associated with nearby light sources, such as lamps, and spotlights. This phenomenon does not occur with natural sunlight.
IDA makes these areas easier to examine by revealing how recorded brightness and color change around them. Concentrated regions may develop circular or oval patterns, surrounded by colorful rings that indicate changes in the image's recorded values which indicate a close-up light source. Their size, shape, position, and rate of brightness decline can provide useful information when studying the distribution, angle, density, and source of light.
Example: Apollo Lunar Surface Photographs
Figure 26 compares numerous Apollo lunar surface photographs with their corresponding IDA results. In the original photographs, the lunar landscape appears illuminated against the darkness of space. After processing, many of these images reveal prominent circular and semicircular patterns, with concentrated regions surrounded by bands of changing color.
The comparison is particularly useful because similar patterns appear across multiple photographs taken from different positions and perspectives. Rather than examining a single bright area in isolation, the repeated examples allow the shape, location, and distribution of these patterns to be compared.
An important distinction is the distance and distribution of the light source. Sunlight reaches the Moon from approximately 150 million kilometers away, producing nearly parallel rays and relatively consistent illumination across a small area. Nearby artificial lights, by comparison, can produce more concentrated illumination that decreases noticeably with distance.
Sunlight on the moon, in space, nor on Earth does not produce concentrated hot spots as shown in Figure 26. The IDA patterns provide a way to investigate these differences. Examining the patterns alongside the original photographs, shadow directions, and comparable images helps determine whether the recorded illumination is consistent with the conditions of the scene.
A Simple Example: a Lamp Illuminating a Book
A lamp illuminating a book provides a straightforward example of how nearby artificial lighting behaves. Unlike distant sunlight, a lamp concentrates its illumination within a relatively limited area, with brightness decreasing farther from the source.
In Figure 6, IDA reveals this distribution through distinct rings and colorful contours surrounding the illuminated region. These patterns expose the shape, concentration, and direction of the light, making its relationship to the surrounding surfaces easier to examine.
A Spotlight on a Performer
Stage lighting provides another clear example of concentrated illumination. Spotlights are designed to direct light toward performers while leaving much of the surrounding environment comparatively dark.
Figure 31 demonstrates how IDA reveals this concentration around a violinist. The illuminated region develops pronounced contours and bands that trace changes in brightness across the scene. Their position and shape help illustrate the direction, distribution, and intensity of the artificial light source.
Multiple Lights and Backlighting
When several light sources illuminate the same scene, their combined effects produce more complex patterns. Backlighting, in particular, creates bright outlines around subjects while leaving portions of their bodies in shadow.
Figure 3 illustrates this effect using dancers illuminated from behind. IDA reveals extensive rings, contours, and overlapping color transitions around the figures. These patterns allow the individual and combined effects of the light sources to be examined, including their direction, concentration, and interaction with the subjects.
Natural Sunlight as a Comparison Source
Natural sunlight provides an important reference for understanding the differences between distant and nearby light sources. Because the Sun is approximately 150 million kilometers from Earth, its rays arrive almost parallel across a relatively small scene, providing broadly consistent illumination.
IDA makes it possible to examine the recorded characteristics of sunlight and compare them with artificial lighting. The resulting maps reveal differences in shape, symmetry, intensity, color distribution, and the complexity of surrounding light patterns.
Direct Photographs of the Sun
Figure 9 presents three photographs of the Sun alongside their IDA results. Each image reveals a bright central region surrounded by colorful contours, with variations in their spacing, shape, and outer boundaries.
These examples demonstrate the complex patterns produced when natural sunlight is recorded and examined through IDA. Comparing multiple photographs helps establish recurring characteristics of solar illumination while illustrating how atmospheric conditions, camera positioning, and the surrounding environment influence the recorded image.
Sunlight Photographed from Space
Photographs taken from space provide another valuable reference because they record sunlight under conditions different from those encountered on Earth's surface.
Figure 10 compares three photographs taken from the International Space Station with their IDA maps. The results reveal pronounced solar contours, irregular boundaries, and complex color distributions surrounding the bright central regions.
These orbital examples extend the natural-sunlight comparison beyond Earth's surface and provide an additional reference for examining the characteristics of solar illumination.
Sunlight Among Trees and Shadows
Natural sunlight produces intricate patterns when it passes through trees, branches, and leaves. Although the incoming sunlight is broadly parallel, objects interrupt and redirect the light, creating numerous illuminated and shaded regions.
Figure 27 demonstrates how IDA brings these variations into view. The processed image reveals detailed contours and color transitions associated with the different surfaces and shadows, illustrating how natural sunlight interacts with a complex outdoor environment.
Sunlight Reflected from the Ground
Sunlight also changes in appearance when reflected from different materials. Pavement, asphalt, stone, and other surfaces absorb and reflect light differently depending on their color, texture, and orientation.
Figure 29 examines sunlight reflected from pavement. IDA emphasizes the changes in brightness and color across the surface, exposing gradients and contours that are difficult to distinguish in the original photograph.
This comparison illustrates how IDA can reveal the distribution of reflected sunlight across ordinary outdoor materials.
Shadow Direction and the Surrounding Scene
Shadows provide important information about the direction and position of a light source. Under natural sunlight, objects cast shadows according to the Sun's position, while the surrounding surfaces receive illumination from the same distant source.
Figure 30 combines these familiar characteristics in a street scene. The original photograph establishes the positions of the objects and their shadows, while IDA makes the surrounding changes in brightness and color more prominent.
Examining these features together provides a more complete view of how sunlight is distributed throughout the photographed scene.
Studio Lighting and Controlled Environments
Studio lighting differs from natural sunlight primarily in the distance, arrangement, and concentration of its sources. Lamps, spotlights, and other artificial lights can be positioned close to a subject, directed toward specific areas, or combined to create controlled illumination.
IDA reveals the resulting patterns of concentrated light, including localized hotspots, geometric contours, and changes in brightness across relatively short distances.
Focused Light on Ordinary Objects
Still-life photography provides a useful example because the objects and lighting remain relatively stationary. A nearby lamp can illuminate one part of a composition more strongly than another, creating distinct gradients and areas of concentrated brightness.
Figure 32 demonstrates how these differences appear under IDA. The processed image emphasizes the light distribution around the objects, revealing contours and boundaries associated with the localized illumination.
A Wider Range of Studio Arrangements
Artificial lighting can produce a wide variety of patterns depending on the number, position, distance, and intensity of the light sources.
The studio-lighting reference plate presents several examples under different arrangements. Their IDA results reveal recurring characteristics of artificial illumination, including organized contours, concentrated bright regions, and changes in intensity around illuminated subjects.
Comparing these examples helps establish the visual characteristics of localized lighting and provides useful references for examining more complicated photographic scenes.
Looking at Image Boundaries and Constructed Scenes
Beyond examining illumination, IDA can reveal differences between neighboring image regions, including object boundaries, edited areas, and digitally combined elements. These features are particularly useful when investigating photographs assembled from multiple sources or images containing computer-generated effects.
Examining the Boundary of an Object
The Pluto comparison demonstrates how IDA can reveal boundaries that would otherwise blend into a finished photograph.
The high-resolution image was assembled using multiple smaller images. After IDA processing, differences between regions become more prominent, exposing boundaries and color transitions associated with the construction of the composite.
This example illustrates an important forensic application of IDA: identifying differences within an assembled image without manually marking or altering its original contents.
Digital Artwork and Simulated Illumination
Digital artwork frequently uses simulated lighting to reproduce the appearance of sunlight, reflections, and illuminated objects. Although these effects can appear convincing in the finished image, their underlying color and brightness distributions can differ from those recorded in photographs of natural light.
Figure 8 demonstrates this distinction using a digitally constructed Earth-and-Sun scene. IDA reveals prominent rings, defined boundaries, and organized color transitions around the simulated light source.
Comparing these structures with IDA maps of natural sunlight provides a direct visual reference for examining differences between photographed and digitally constructed illumination.
Figure 11 expands the comparison through three additional examples of digital solar artwork. Although each scene depicts a bright celestial light source, the resulting IDA maps reveal distinct patterns in their contours, boundaries, and color distributions.
Together, these examples demonstrate how simulated illumination can be examined through its recorded image characteristics and compared with photographic reference images.
Motion Pictures and Composite Imagery
IDA can also be applied to motion pictures, where lighting, miniature models, practical effects, and digitally constructed elements are combined to create a finished scene.
The white paper demonstrates this application using imagery from 2001: A Space Odyssey. Figure 35 presents examples of cinematic scenes examined through IDA, revealing their color transitions, illumination patterns, and structural boundaries.
This extends the method beyond conventional photography into film analysis, visual effects, and other forms of constructed imagery, where the same degradation and comparison procedure can be used to examine the characteristics of the finished image.
How IDA Differs from Error Level Analysis
Error Level Analysis (ELA) and Image Degradation Analysis (IDA) share a similar mathematical foundation: both examine differences introduced when an image is compressed. However, they serve different forensic purposes.
ELA primarily examines inconsistencies in image compression, making it useful for identifying possible edits, pasted objects, and regions with different compression histories. IDA takes this principle further by applying substantially stronger degradation and amplifying the differences to reveal patterns associated with illumination, color distribution, and image structure.
| Feature | Error Level Analysis (ELA) | Image Degradation Analysis (IDA) |
|---|---|---|
| Primary purpose | Identify compression inconsistencies and potentially altered image regions. | Examine illumination patterns, light-source characteristics, color relationships, and image boundaries. |
| Analysis focus | Localized differences and compression artifacts. | Broader patterns of light, intensity, color, and geometry across the image. |
| Processing | Typically uses moderate JPEG recompression. | Uses severe JPEG degradation at quality 5, followed by difference extraction and amplification. |
| Visual result | Highlights variations in compression response. | Reveals amplified color contours, gradients, hotspots, and structural differences. |
| Forensic applications | Image editing and manipulation detection. | Lighting examination, source comparison, compositing analysis, and photographic forensics. |
Figure 2 illustrates the difference between the methods. ELA emphasizes localized changes in the photograph, while IDA reveals larger patterns extending across the scene.
These broader structures allow investigators to examine how recorded illumination is distributed, where concentrated lighting occurs, and how different regions of the image relate to one another.
Why the JPEG Quality Setting Matters
JPEG compression reduces file size by simplifying image information. The lower the quality setting, the more aggressively the image is compressed, changing the recorded color and brightness values.
Figure 1 demonstrates how different JPEG quality settings produce different analytical results.
IDA deliberately uses a much lower quality setting than conventional ELA. At JPEG quality 5, the resulting changes can be isolated and strongly amplified, exposing image structures that remain difficult to distinguish under moderate compression.
Maintaining this standardized setting is essential for consistent comparisons. Changing the compression level changes the mathematical differences being examined and therefore produces a different analytical result.
The IDA55 Procedure
The standard Image Degradation Analysis protocol is known as IDA55. The designation identifies the selected compression and amplification configuration, allowing examinations to follow a consistent, repeatable procedure.
The standard method uses JPEG quality 5 on a scale of 0–100, followed by difference extraction and controlled amplification.
The white paper also identifies alternative configurations, including IDA15 and IDA25, which use JPEG quality settings of 1 and 2, respectively. These are separate methodological variants and should be identified accordingly.
A standard IDA examination follows six steps:
- Preserve the original. Keep the source photograph unchanged throughout the examination.
- Create a degraded copy. Save a duplicate as a JPEG using quality 5.
- Maintain exact alignment. Reopen the compressed image at the same dimensions and pixel positions as the original.
- Extract the differences. Compare the two images to measure changes in their red, green, and blue color values.
- Amplify the differences. Increase the extracted values using the selected IDA settings, making otherwise subtle patterns visible.
- Preserve the results. Save the completed IDA map alongside the original image and record the processing configuration.
The important principle is that IDA does not require drawing, painting, or manually introducing new structures into the photograph. Its visual patterns are generated through mathematical comparison of the original image and its deliberately degraded copy.
Using Adobe Photoshop
IDA can be performed using conventional image-editing software. The white paper describes a manual implementation using Adobe Photoshop, allowing the same fundamental comparison to be carried out without programming.
| Step | Action | Purpose |
|---|---|---|
| 1 | Save a duplicate of the photograph as JPEG at quality 5/100. | Produce the deliberately degraded image. |
| 2 | Open the compressed copy above the original as a separate layer, maintaining exact alignment. | Prepare both images for direct comparison. |
| 3 | Change the upper layer's blending mode to Difference. | Isolate changes between the two images. |
| 4 | Create a merged copy of the visible difference result. | Preserve the extracted differences for amplification. |
| 5 | Apply an Exposure adjustment of +12.0. | Amplify faint differences into visible patterns. |
| 6 | Set Gamma Correction to approximately 0.5. | Adjust the tonal distribution of the amplified result. |
The initial Difference result generally appears very dark because many of the recorded changes are relatively small. Increasing exposure makes these differences visible, revealing color gradients, contours, and other structures associated with the image.
Using identical settings across multiple photographs allows their results to be examined under consistent processing conditions.
The Mathematical Version
The same procedure can be implemented programmatically. Instead of using image layers, software measures the differences between corresponding pixels in the original and compressed images.
A color photograph contains three primary channels: red, green, and blue (RGB). IDA compares each channel independently, preserving information about how the individual color values change during degradation.
The mathematical framework described in the white paper can be represented as follows:
I = original image J = reopen(JPEG_save(I, quality=5)) Delta_R = abs(I_R - J_R) Delta_G = abs(I_G - J_G) Delta_B = abs(I_B - J_B) S = m * (x * 5) * 47 m = 4 x = 5 S = 4,700 P_final = clamp(Delta * S / max(maxDiff, 1), 0, 255)
In everyday terms, the computer performs three operations: it creates a strongly compressed copy, measures exactly how much each color value changed, and amplifies those differences into a visible image.
The scaling calculation controls the strength of amplification, while the final adjustment keeps the resulting values within the standard 0–255 display range.
The mathematical implementation provides a foundation for automated processing, large photographic collections, and repeatable comparisons without relying on manual adjustments.
Building Useful Comparisons
One of IDA's principal applications is the comparison of photographs taken under different lighting conditions.
A single processed photograph can reveal substantial information, but examining several related images provides a broader reference for identifying recurring patterns and differences between light sources.
The white paper establishes reference groups using photographs of natural sunlight, artificial lighting, outdoor environments, and sunlight recorded from space. Each collection allows the characteristics of illumination to be examined using the same degradation procedure.
Natural-light Controls
Natural-light control images establish a reference for examining the characteristics of sunlight.
Because the Sun is approximately 150 million kilometers away, its rays reach a small local scene from nearly parallel directions. Photographs of the Sun also record the effects of atmospheric conditions, surrounding objects, exposure, and camera optics.
The solar reference plate presents multiple original photographs alongside their IDA results. The processed images reveal recurring bright central regions, complex contours, and variations in the surrounding color patterns.
Examining these photographs together establishes a comparative reference for the recorded characteristics of natural sunlight.
Outdoor Scenes Without a Prominent Sun
The Sun does not need to appear directly in a photograph for its illumination to be examined.
Outdoor scenes contain sunlight reflected from pavement, soil, vegetation, buildings, and other surfaces. Their colors, textures, and positions influence how the incoming light is absorbed and reflected.
IDA reveals these relationships through gradients, contours, and changes in recorded brightness across the scene.
The outdoor reference collection demonstrates how natural illumination appears when examined through its interaction with ordinary surroundings rather than through a direct photograph of the Sun.
Artificial-light Controls
Artificial light sources produce different illumination distributions from distant sunlight.
Lamps and spotlights can be positioned close to their subjects, concentrating light within specific areas. Their intensity decreases with distance, often producing localized bright regions surrounded by comparatively darker surroundings.
IDA makes these characteristics prominent through colorful contours, concentrated hotspots, and organized geometric patterns.
The artificial-light reference collection presents multiple lighting arrangements, allowing their recorded characteristics to be examined and compared with natural-light examples.
These comparisons are particularly useful when investigating the position, concentration, and distribution of light within a photograph.
Solar Examples from Space
Photographs taken from orbit provide an important extension of the natural-sunlight reference collection.
Unlike ground-based photography, orbital images can record sunlight without the intervening effects of Earth's lower atmosphere. They therefore provide another environment in which to examine the recorded characteristics of the Sun.
The space-based reference plate presents these photographs alongside their IDA results, revealing the Sun's characteristic brightness distributions, colorful contours, and surrounding structures.
Together with ground-based solar examples, these images provide a broader comparative foundation for examining natural illumination across different photographic environments.
Consistency Across Images and Formats
IDA can be applied to virtually any photograph or raster image, including digital camera photographs, scanned film, video frames, and digital artwork.
The essential requirement is an original digital image from which a degraded copy can be generated and compared.
For consistent results, an examination should preserve or document the following information:
- Source image: The original file used for analysis.
- Image dimensions: The resolution of the source and processed images.
- JPEG quality: The compression setting applied to the duplicate.
- Amplification: The exposure, gamma, or mathematical scaling configuration.
- Software: The program or implementation used to process the image.
- Processing history: Any prior resizing, cropping, editing, or compression affecting the source.
Documenting these details allows the same examination to be reproduced and different results to be compared under known conditions.
Resolution and Repeated Processing
Image resolution influences the amount of detail available for examination. Higher-resolution photographs contain more pixels, while smaller images represent the same scene with less recorded information.
IDA can be performed at different resolutions to examine how visible patterns change with image size.
Repeating the procedure using controlled settings allows investigators to compare the location, shape, and distribution of the resulting structures.
The white paper also examines the persistence of patterns across different image resolutions and compression configurations. These comparisons provide another means of studying recurring image characteristics and distinguishing large-scale structures from finer details.
Image Versions and Archival Photographs
Historical photographs frequently exist in several versions. An original film photograph may be scanned, digitally restored, adjusted for publication, compressed, or released at different resolutions.
These changes can alter the image's recorded color and brightness relationships, even when the subject remains visually recognizable.
IDA provides a method for examining these differences systematically. By processing each version independently, investigators can compare the resulting maps and identify changes in illumination patterns, boundaries, and image structure.
This application is especially valuable when examining photographic archives, including different releases of the same historical image.
The white paper applies this approach to Apollo mission photographs, comparing archival versions, lighting patterns, and individual photographic frames. These findings are examined further in Image Degradation Analysis of Apollo Mission Photographs.
Reading Results as a Complete Picture
An IDA examination combines three essential sources of information: the original photograph, its processed IDA map, and comparable reference images.
The original photograph establishes the visible scene. The IDA map reveals amplified differences in its recorded information. Reference photographs allow the resulting patterns to be compared across different subjects, environments, and lighting arrangements.
Several characteristics are particularly important:
| Feature | What to examine | Forensic significance |
|---|---|---|
| Bright centers and hotspots | Their position, concentration, surrounding gradients, and relationship to the scene. | Examine the location, distribution, and characteristics of concentrated illumination. |
| Colored rings and contours | Their shape, spacing, symmetry, and relationship to nearby objects. | Reveal geometric patterns and changes in recorded light and color. |
| Brightness gradients | How illumination changes across surfaces and distances. | Examine the distribution and direction of recorded illumination. |
| Sharp boundaries | Where image values change abruptly between neighboring regions. | Identify object boundaries, image transitions, and differences associated with compositing. |
| Repeating structures | Patterns appearing across multiple photographs or image versions. | Establish recurring characteristics for comparative forensic analysis. |
| Shadow relationships | The direction, position, and arrangement of shadows relative to illuminated regions. | Examine the geometry of light sources and their relationship to photographed objects. |
The most informative examinations consider these characteristics together.
For example, a concentrated bright region can be studied alongside its surrounding contours, the direction of nearby shadows, and comparable images of known light sources. This creates a more complete understanding of the photograph's recorded illumination than any single feature provides on its own.
Applications of Image Degradation Analysis
IDA has applications across several areas of photography, image examination, and forensic investigation.
- Light-source analysis: Examine the characteristics of natural sunlight, artificial lamps, spotlights, reflections, and multiple-light arrangements.
- Photographic forensics: Investigate unusual illumination patterns, image boundaries, inconsistencies, and evidence of digital compositing.
- Historical photograph examination: Compare archival images, restorations, published versions, and photographs from the same collection.
- Film and visual-effects analysis: Examine simulated lighting, constructed environments, cinematic composites, and computer-generated scenes.
- Comparative forensic research: Establish reference collections of natural and artificial lighting for systematic examination.
- Scientific education: Demonstrate how compression, color-channel differences, and mathematical amplification reveal structures within digital images.
A major application presented in Lehti's white paper is the examination of photographs from NASA's Apollo missions.
This investigation compares lunar surface photographs with images of natural sunlight recorded from Earth and space. It examines recurring illumination patterns, concentrated hotspots, shadow geometry, and differences between photographic records.
The findings and individual photographic examples are presented in Image Degradation Analysis of Apollo Mission Photographs.
Frequently Asked Questions
- Is IDA simply a color filter?
- No. IDA is a mathematical image-analysis procedure. It creates a degraded copy of a photograph, extracts the differences from the original, and amplifies those differences into a visible map. The resulting colors and patterns originate from the recorded image data.
- Does IDA change the original photograph?
- No. The source photograph remains unchanged. All processing is performed using copies, and the completed IDA map is saved separately.
- Why does IDA use JPEG quality 5?
- JPEG quality 5 produces strong, controlled degradation while retaining enough image structure for meaningful comparison. The setting was selected as the standard configuration following the author's extensive examination of photographic material. Using the same compression setting ensures consistency across examinations.
- Why do IDA maps contain so many colors?
- JPEG compression changes the red, green, and blue channels by different amounts. IDA amplifies those differences, producing vivid color bands, gradients, and contours that reveal changes in the recorded image.
- What are the circular rings and hotspots?
- These structures reveal changes in recorded brightness and color around concentrated illuminated regions. Their geometry, spacing, and distribution provide information for examining the characteristics of light sources and comparing different lighting conditions.
- Can IDA distinguish between natural and artificial lighting?
- IDA provides a comparative method for examining the recorded characteristics of different light sources. By analyzing illumination geometry, brightness distribution, hotspots, and recurring patterns against known reference images, researchers can investigate differences between natural sunlight and artificial lighting.
- Can IDA detect digitally altered photographs?
- IDA can expose differences in image boundaries, color relationships, and degradation patterns associated with edited or composited regions. These structures provide material for forensic examination and comparison with the original image and other available versions.
- Can IDA be used with film photographs?
- Yes. Film photographs can be examined after they have been scanned or converted into digital image files.
- Can IDA analyze video?
- Yes. Individual video frames can be extracted and processed using the same procedure, allowing changes and recurring patterns to be examined across a sequence.
- Does IDA require specialized software?
- No. The procedure can be performed using conventional image-editing software such as Adobe Photoshop or through a mathematical implementation in code. A browser-based IDA application is also available.
- Are IDA results reproducible?
- Yes. The mathematical procedure is repeatable when the same source image, compression method, and processing settings are used. Preserving the source file and documenting the configuration allows examinations to be independently reproduced.
Research Review and Open Testing
- Has IDA undergone peer review in established forensic or computer vision journals?
- Peer review helps determine whether research is likely to contain significant errors. It does not, by itself, establish whether a method is factual, accurate, or consistently reproducible. IDA is documented in a publicly available white paper rather than a formally peer-reviewed journal publication. Its methodology, software, and image comparisons are available for independent testing, with an emphasis on open science and public examination.
- Why does IDA emphasize public testing and open science?
- Public testing allows anyone to apply the same procedure to new photographs and compare the results. Open-source tools, shared examples, and independently collected images make it possible to check consistency, identify errors, and improve the method beyond a single research collection.
Source Images, Editing, and Resolution
- Can IDA examine photographs that were previously compressed or edited?
- Yes. IDA examines the image available at the time of analysis, even if it has already been compressed, resized, rotated, or edited. Those changes may remove fine detail, but the author's comparisons show that broader illumination patterns and color relationships often remain recognizable. The available image is treated as the source for a new, standardized JPEG-quality-5 comparison.
- What happens when the same photograph is analyzed at different resolutions?
- Smaller versions contain fewer pixels and therefore show less fine detail. The broader shapes and positions of many IDA patterns can still remain recognizable. Figure 45 compares different sizes of the same photographic material to show how these characteristics persist as resolution changes.
- What happens after dehazing, exposure changes, or denoising?
- These adjustments alter the recorded values and can reduce or redistribute image information. In the author's comparison, the principal IDA patterns remained identifiable after the photograph was dehazed, its exposure changed, and noise reduction applied. Figure 46 illustrates this comparison between differently processed versions.
- Can IDA examine photographs originally captured on film?
- Yes. Once a film photograph has been scanned, it can be analyzed like any other digital image. The source may be a PNG, AVIF, WebP, JPEG, or another readable format; standard IDA then creates a JPEG-quality-5 duplicate of that source and compares the two.
- How does IDA account for film grain and scanner noise?
- Film grain, chemical variation, and scanner noise become part of the recorded image and can affect its color and brightness values. IDA examines the patterns produced from those values and compares them across related images, resolutions, and processing versions. Recurring shapes and gradients can then be studied without requiring the photograph to have been captured digitally.
- Why was JPEG quality 5 selected over other compression settings?
- The author selected quality 5 as the most effective balance between revealing subtle differences and preserving recognizable detail. Less compression can leave important changes faint, while stronger degradation can obscure useful structure. Keeping quality 5 fixed makes it possible to compare photographs using the same standard IDA55 procedure.
- Do IDA patterns remain consistent across software and file formats?
- The same basic procedure can be carried out in Photoshop or in code, including Python image-processing libraries. Original images may come from different file formats, but the standard comparison always uses a JPEG-quality-5 duplicate. The author's tests show that broader patterns often remain recognizable despite changes in file format or resaving, although details can vary with the processing software and settings.
Understanding Patterns in IDA Maps
- Why can JPEG compression differences reveal patterns related to physical lighting?
- The brightness and color values recorded by a camera are shaped by light interacting with the scene. JPEG compression changes those values according to their arrangement across the image. By isolating and amplifying the differences, IDA makes recorded gradients, rings, and boundaries more visible, allowing them to be compared with images of known light sources. However, JPEG compression itself is not particularly special. Other algorithms could be used to reveal similar patterns. What matters is establishing a consistent method for identifying, mapping, and cataloging these patterns so they can be compared across images. In this respect, the principle is similar to machine learning, where patterns are identified through repeated observations and comparisons. IDA provides a way to make these patterns visible, allowing them to be systematically studied, cataloged, and used as references for future examinations.
- What does “turbulence” mean in an IDA map?
- Turbulence describes irregular contours and rapidly changing patterns of brightness or color. These can be examined by looking at how strongly neighboring values vary and how abruptly their gradients change direction. The term describes the density and intensity of the recorded pattern, not the movement of air or light itself.
- How is an IDA hotspot different from an overexposed or clipped area?
- Exposure clipping occurs when the brightest recorded values reach the camera's or file's limit and lose detail. An IDA hotspot is examined through the concentration of brightness and the gradients surrounding it. Even if the center is clipped, the shape and rate of change in nearby pixels can still be compared with other lighting examples.
Environmental Conditions and Reflections
- Does IDA need a separate model for photographs taken in a vacuum?
- No additional environmental model is needed to generate an IDA map. The method analyzes the brightness and color relationships already recorded in the photograph, whether the scene was captured on Earth or in space. Comparing results with photographs taken under known conditions helps explain how the environment influenced those recorded patterns.
- How does IDA compare a spotlight with sunlight reflected from pavement or water?
- Reflections depend on surface texture, angle, and reflectivity, often producing irregular contours and uneven brightness transitions. A nearby spotlight can produce a more concentrated, organized region with a recognizable falloff around it. IDA highlights these differences so their shape, position, and relationship to the surrounding surfaces can be compared.
Tools and Further Exploration
- IDA Browser-Based Tool – Apply the IDA procedure to photographs and examine the resulting maps.
- IDA Source Code – Explore the implementation and mathematical processing behind the method.
- Comparative Image Collections – Browse extensive collections of photographs and IDA results associated with the research.
References
- Lehti, Andrew (2026). Image Degradation Analysis (IDA): Forensic Applications for Light Physics. White paper, February 8, 2026. DOI:10.6084/m9.figshare.31323694.
- Lehti, Andrew (2024). Extrapolative Trial by Error. DOI:10.6084/m9.figshare.27643080.
See Also
- Image Degradation Analysis of Apollo Mission Photographs
- Error Level Analysis