Image & Design Analyzer
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Analyzing Image Topology
Mapping Data Vectors
Processing Visual Metrics
Decoding Color Signatures
Initializing Design Analysis
Image Analysis
1. Basic Information
Dimensions
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2. Color Analysis
Color Distribution
RGB Analysis
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This analysis breaks down the image into its Red, Green, and Blue color channels.
Each percentage represents the proportion of dominant pixels in that channel.
A balanced distribution indicates an image with varied colors,
while a dominance suggests a specific chromatic tone.
Average Brightness
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Scale: 0 (dark) to 255 (bright)
Average brightness measures the overall luminous intensity of the image.
A value close to 0 indicates a dark image, while a value close to 255
suggests a very bright image. This metric helps understand the
overall mood and contrast of the image.
Average Saturation
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Color intensity measurement
Saturation measures the intensity and purity of colors in the image.
A high percentage indicates vivid and intense colors,
while a low percentage suggests more pastel or washed-out colors.
This metric captures the chromatic richness of the image.
3. Design Analysis
Color Harmony
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Analysis of color relationships
Color harmony evaluates how well colors interact and complement each other in the image.
It identifies color relationships like complementary, analogous, or triadic schemes.
A harmonious color palette creates visual balance and aesthetic appeal,
while discordant colors can create tension or visual discomfort.
Contrast Ratio
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Difference between lightest and darkest areas
Contrast ratio measures the difference in luminance between the brightest and darkest parts of the image.
A high contrast ratio indicates clear differentiation between elements,
which can enhance readability and visual impact.
This metric is crucial in design for creating depth and visual hierarchy.
Visual Weight
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Distribution of visual elements
Visual weight describes how different elements attract the viewer's attention.
It analyzes the distribution of visual elements based on size, color, and positioning.
A balanced visual weight guides the viewer's eye through the image,
creating a sense of composition and intentional design.
4. Composition Analysis
Rule of Thirds
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Alignment with compositional grid
The Rule of Thirds divides the image into a 3x3 grid, analyzing how key elements align with these gridlines or intersections.
Images with main subjects positioned near these points tend to be more balanced and visually engaging.
A high percentage indicates that important visual elements are strategically placed,
creating a more dynamic and aesthetically pleasing composition.
Symmetry Score
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0 (asymmetric) to 1 (symmetric)
Symmetry score measures how evenly elements are distributed across the image's central axis.
A score closer to 1 indicates perfect mirror-like balance, while lower scores suggest more asymmetrical compositions.
Symmetry can create a sense of harmony, stability, and intentional design,
though not all compelling images require perfect symmetry.
Balance Score
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Distribution of visual weight
Balance score evaluates how visual elements are distributed across the image plane.
It considers color, size, and positioning of different elements to determine overall compositional equilibrium.
A high balance score suggests that no single area dominates excessively,
creating a harmonious and visually comfortable image.
5. Visualizations
RGB Distribution
Red
Green
Blue
The RGB Distribution histogram provides a graphical representation of color intensity across the image.
Each color channel (Red, Green, Blue) is plotted separately, showing the frequency of pixel values from 0 to 255.
Peaks indicate dominant color intensities, helping to understand the color composition and tonal range.
This visualization reveals color distribution, helping photographers and designers analyze image color characteristics.
Edge Detection
Strong Edges
Weak Edges
No Edges
Edge Detection highlights the boundaries and structural outlines within the image.
By identifying areas of significant color or intensity transitions, this visualization reveals the image's structural complexity.
Bright areas represent sharp edges and strong transitions, while darker areas indicate smoother regions.
This technique is crucial in understanding image composition, object boundaries, and visual complexity.
6. Texture Analysis
Texture Roughness
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Measure of surface irregularity
Texture roughness quantifies the surface variability and pixel intensity variations within the image.
A high roughness value indicates a complex, highly detailed texture with significant pixel-to-pixel differences.
Lower values suggest smoother, more uniform surfaces. This metric helps understand the tactile
and structural characteristics of the visual elements in the image.
Texture Homogeneity
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Uniformity of texture patterns
Texture homogeneity measures the consistency and predictability of texture patterns across the image.
A high homogeneity score indicates a uniform, regular texture with minimal variations.
Lower scores suggest more diverse and complex textural elements.
This metric provides insights into the image's surface smoothness and textural complexity.
Detail Scale
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Micro vs macro texture levels
Detail scale classifies the texture's predominant characteristic between micro and macro levels.
Micro textures represent fine, intricate details, like the smoothness of silk or glass.
Macro textures indicate larger, more prominent surface variations, such as tree bark or rocky terrain.
This metric helps understand the scale and granularity of the image's textural elements.
7. Emotional Analysis
Dominant Sentiment
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Overall emotional tone
Dominant sentiment analyzes the emotional atmosphere of the image by examining color, brightness,
and composition. It categorizes the overall mood into emotional states like Serene, Intense, Joyful,
or Neutral. This metric interprets visual elements to gauge the psychological and emotional impact
of the image, helping to understand its intrinsic emotional communication.
Emotional Intensity
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Strength of emotional expression
Emotional intensity measures the power and depth of the image's emotional expression.
Ranging from 0 to 1, this metric quantifies how strongly the image conveys its emotional tone.
A high intensity suggests a powerful, evocative image that strongly communicates its emotional essence,
while lower intensities indicate more subtle or subdued emotional representations.
Color Emotion Mapping
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Emotional associations of colors
Color emotion mapping connects the dominant colors in the image to specific emotional states.
Different colors inherently evoke various psychological responses: reds can signify passion,
blues suggest calmness, greens represent hope, and so on. This metric translates color psychology
into a nuanced understanding of the image's emotional landscape.
8. Advanced Technical Metrics
Signal-to-Noise Ratio
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Image quality indicator
Signal-to-Noise Ratio (SNR) measures the clarity and quality of the image by comparing
meaningful image data (signal) to random variations (noise). A higher SNR indicates
a cleaner, more precise image with less digital artifacts or compression distortions.
This metric helps assess the technical quality and preservation of image details.
Compression Level
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Image data compression rate
Compression level evaluates how much the image data has been compressed, which can impact
image quality and file size. High compression reduces file size but may introduce artifacts
and reduce image clarity. This metric helps understand the trade-off between image storage
efficiency and visual fidelity.
Color Space
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sRGB, Adobe RGB, etc.
Color space determines how colors are represented and interpreted in the image.
Different color spaces like sRGB, Adobe RGB, or ProPhoto RGB have varying color gamuts
and capabilities for reproducing color ranges. This metric identifies the color encoding
standard used, which affects color accuracy and reproduction across different devices.
9. Geometric Analysis
Shape Count
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Number of distinct shapes
Shape count analyzes the number of distinct geometric forms within the image.
This metric helps understand the compositional complexity by identifying and counting
unique shapes, from simple geometric primitives to more intricate structures.
A higher shape count suggests a more complex and visually rich image composition.
Shape Distribution
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Types of geometric forms
Shape distribution categorizes the types and prevalence of geometric forms in the image.
It classifies shapes into categories like simple, moderate, or complex, providing insights
into the structural complexity and visual design of the image.
This metric helps understand the underlying geometric patterns and composition.
Contour Complexity
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Complexity of object boundaries
Contour complexity measures the intricacy and variation of object boundaries within the image.
A high complexity score indicates detailed, irregular, or highly defined edges,
while a low score suggests smoother, more uniform object boundaries.
This metric provides insights into the level of detail and structural definition in the image.
10. AI-Powered Analysis
Content Classification
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Image type detection
Content classification uses advanced machine learning algorithms to categorize the image
into predefined types such as landscape, portrait, urban scene, or abstract composition.
This metric goes beyond simple visual analysis, attempting to understand the fundamental
nature and subject matter of the image using trained neural networks.
Object Detection
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Identified objects
Object detection uses artificial intelligence to identify and locate specific objects
within the image. This advanced technique attempts to recognize and label distinct
elements, providing insights into the image's content beyond simple color and texture analysis.
The metric reveals the complexity and informational density of the visual scene.
Artistic Style
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Estimated art style
Artistic style estimation employs machine learning to categorize the image's visual
characteristics into artistic genres or styles. By analyzing color palette, edge complexity,
and compositional elements, this metric attempts to identify whether the image
resembles minimalist, expressionist, contemporary, or other artistic approaches.
11. Additional Visualizations
Texture Heatmap
Smooth
Medium
Rough
The Texture Heatmap provides a color-coded representation of texture variations across the image.
Using a gradient from blue (low variation) to red (high variation), this visualization reveals
the complexity and textural nuances of different image regions.
Emotional Color Spectrum
Serene
Calm
Peaceful
Hopeful
Joyful
Energetic
Intense
Passionate
Pensive
Melancholic
Angry
Neutral
The Emotional Color Spectrum creates a radial gradient that captures the dominant emotional tone
of the image. Using color psychology principles, this visualization translates the image's
emotional essence into a visual representation. The central color and gradient intensity reflect
the emotional characteristics detected during the image analysis, providing an intuitive
and immediate sense of the image's psychological impact.
Analysis Methodology
Image Dimensions
- Width (px) × Height (px)
- Total Pixels = Width × Height
- Aspect Ratio = Width ÷ Height
Example Calculation:
1920×1080 image:
Total pixels = 2,073,600
Aspect ratio = 1.78 (16:9)
Standard Resolutions
- HD: 1280×720
- Full HD: 1920×1080
- 4K: 3840×2160
RGB Channel Analysis
Per-Pixel Analysis:
RGB Values: 0-255 per channel
Total Colors = 256³ possible combinations
Color Depth = 24 bits (8 bits per channel)
Brightness Calculation
Formula:
Brightness = (R + G + B) ÷ 3
Average = Σ(brightness) ÷ total_pixels
Range: 0 (black) to 255 (white)
Saturation Measurement
Formula:
Max = maximum(R,G,B)
Min = minimum(R,G,B)
Saturation = ((Max - Min) ÷ Max) × 100%
Example Values:
- Pure Red (255,0,0): 100% saturation
- Pure Gray (128,128,128): 0% saturation
- Pale Red (255,128,128): 50% saturation
Color Harmony
- Complementary (180° apart):
Red-Green, Blue-Orange
- Analogous (30° adjacent):
Blue-Purple-Red
- Triadic (120° spacing):
Red-Blue-Yellow
Contrast Ratio
ContrastRatio = (L1 + 0.05) ÷ (L2 + 0.05)
L1 = luminance of lighter color
L2 = luminance of darker color
WCAG Guidelines:
- Minimum (AA): 4.5:1
- Enhanced (AAA): 7:1
Rule of Thirds
- Grid Creation:
horizontal_lines = [height/3, 2×height/3] vertical_lines = [width/3, 2×width/3] - Interest Point Detection:
for each intersection_point: analyze surrounding 5% area detect high contrast regions
Symmetry Analysis
for each pixel (x,y):
compare with mirror_pixel (width-x, y)
diff = |pixel - mirror_pixel|
total_diff += diff
symmetry = 1 - (total_diff ÷ max_possible_diff)
Balance Score
for each quadrant:
weight = Σ(pixel_intensity × distance_from_center)
balance = 1 - (weight_variance ÷ total_weight)
RGB Histogram
- X-axis: Color intensity (0-255)
- Y-axis: Pixel frequency (normalized)
- Separate curves for R, G, B channels
- Peak analysis for dominant colors
Edge Detection Visualization
- Sobel operator for gradients
- Threshold visualization
- Connected component labeling
- Edge highlighting overlay
Texture Roughness
Calculation Method:
for each pixel neighborhood:
calculate local intensity variations
aggregate total variation
roughness = total_variation / total_pixels
homogeneity = 1 - roughness
Detail Scale Classification
- Micro Texture: Roughness < 0.2
- Mixed Texture: 0.2 ≤ Roughness ≤ 0.8
- Macro Texture: Roughness > 0.8
Texture Examples:
- Smooth Surface (Micro): Silk, Glass
- Complex Surface (Macro): Tree Bark, Rocky Terrain
Emotional Tone Mapping
Emotion Calculation:
Factors:
- Brightness (0-255)
- Saturation (0-100%)
Emotion Mapping:
- High Brightness + Low Saturation = Serene
- Low Brightness + High Saturation = Intense
- Moderate Brightness + Moderate Saturation = Joyful
Color Emotion Associations
- Passionate (Red Dominance):
Bold, Energetic, Powerful
- Hopeful (Green Dominance):
Fresh, Growing, Optimistic
- Calm (Blue Dominance):
Peaceful, Stable, Tranquil
Signal-to-Noise Ratio
Calculation Method:
Calculate pixel-to-pixel variations
Compute average noise level
SNR = 1 / (average_noise + 1)
SNR Interpretation:
- High (> 0.7): Low Noise
- Medium (0.3 - 0.7): Moderate Noise
- Low (< 0.3): High Noise
Compression Level Estimation
Compression Assessment:
- High Compression: Significant pixel variation reduction
- Medium Compression: Moderate variation
- Low Compression: Minimal variation
Shape Detection
Detection Algorithm:
Edge Detection Steps:
1. Identify high-contrast pixel regions
2. Group adjacent high-contrast pixels
3. Extract distinct shape boundaries
Shape Complexity Classification:
- Simple: Few distinct boundaries
- Moderate: Multiple overlapping shapes
- Complex: Dense, intricate boundaries
Contour Complexity
Complexity Measurement:
contour_complexity = edge_pixels / total_pixels
Complexity Levels:
- Low: < 0.2
- Medium: 0.2 - 0.5
- High: > 0.5
Content Classification
Classification Criteria:
Factors:
- Average Brightness
- Color Saturation
- Edge Complexity
Classification Types:
- Landscape/Nature
- Portrait/Dramatic
- Urban/Colorful
- Mixed/Undefined
Artistic Style Estimation
Style Categories:
- Minimalist: Low color variation, simple edges
- Contemporary: Moderate complexity
- Expressionist: High color variation, complex edges
Texture Heatmap
Heatmap Generation:
Color Gradient Mapping:
- Blue: Low texture variation
- Green: Moderate texture
- Red: High texture variation
Visualization Technique:
- Linear gradient representation
- Pixel-level intensity analysis
Emotional Spectrum
Emotion Visualization:
Radial Gradient Technique:
- Center: Dominant Emotional Tone
- Periphery: Color Intensity Fade-out
Emotion Color Mapping:
- Serene: Soft Blue
- Intense: Vibrant Red
- Joyful: Warm Yellow
- Neutral: Muted Gray
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