High-Level Color Extractor (HCE)

• High-level Color Extractor (HCE) is a systematic framework that extracts, models, and refines dominant color properties from digital images using techniques like D-CDEN and channelized binning.
• It leverages perceptually uniform color spaces alongside advanced statistical and geometric methods to boost accuracy in image retrieval, color harmonization, and biometric analysis.
• HCE applications span animation colorization, multispectral imaging, digital art, and biometrics, demonstrating both algorithmic efficiency and practical impact.

A High-level Color Extractor (HCE) refers to a systematic framework, algorithmic module, or device that extracts, models, and manipulates the dominant or semantically relevant color properties from visual data. HCEs encompass feature extraction techniques, machine learning-based pipelines, physical filter arrays, and image decomposition tools, and they serve applications spanning image retrieval, animation colorization, digital art, multispectral imaging, and biometric analysis. HCEs typically focus on either semantic palette extraction, spatial color modeling, dominant color identification, or color purity enhancement, often employing perceptually uniform color spaces and advanced statistical or geometric methods.

1. Algorithmic Foundations and Methodologies

Multiple HCE variants employ advanced methodologies to extract high-level color descriptors beyond basic histograms, including:

• Dynamic Color Distribution Entropy of Neighborhoods (D-CDEN): Improves standard color histograms by integrating spatial information. It adaptively extracts pixel neighborhoods for each color bin by row-by-row scanning and merges adjacent pixels with the same color. The spatial distribution of colors is summarized via a normalized spatial distribution histogram $P'_i$, with entropy $E_i = -\sum_{j=1}^{n_b^i} P'_{ij} \log_2(P'_{ij})$ for $n_b^i$ neighborhoods per color bin. The image descriptor consists of $(h_i, E_i)$ pairs, encapsulating both global color presence and spatial dispersion (Alamdar et al., 2012).
• Channelized Binning: Extracts dominant colors in 24-bit RGB images by iterative binning in each color channel. The algorithm computes the average inter-pixel difference $$p = \frac{1}{n-1} \sum_{i=1}^{n-1} (pixel_i - pixel_{i-1})$$ and refines bins until empty bins are eliminated. A subsequent phase merges bins with similar centroids or insufficient pixel counts. The final centroids become dominant color descriptors. This approach circumvents global quantization/clustering and operates efficiently in the one-dimensional 8-bit channel domain (Algur et al., 2016).
• Palette-based Geometric Decomposition: Computes the RGB convex hull of image pixels and simplifies via edge-collapse to obtain a perceptually representative palette. Image decomposition in 5D (RGBXY) space then assigns mixing weights to each palette element, allowing per-pixel expression as a convex combination of palette colors. Harmonic templates (axis-based in LCh or LC plane) are fit for harmonization and color transfer (Tan et al., 2018).
• Deep Learning-Based Colormap Prediction: Summarizes input images as Lab color histograms, "flattens" extraneous spatial features to a 2D map, and feeds them to a ResNet18-based CNN with atrous spatial pyramid pooling to predict colormap sequences via regression. Post-processing distinguishes discrete and continuous colormaps, applying DBSCAN and Laplacian eigenmaps for refinement. Evaluation uses dynamic time warping (DTW) in Lab space (Yuan et al., 2021).
• K-Means and GAN for Sketch Colorization: Gaussian blur preprocesses sharp edges followed by k-means clustering to quantize dominant colors for outline sketches. A conditional GAN with U-Net generator and PatchGAN discriminator then learns to generate colored sketches from images and sketches, utilizing Lab color transfer and saturation amplification (Manushree et al., 2021).
• Cumulative Color Histogram for Color Counting: Computes histograms per RGB channel, constructs cumulative triplet vectors for the global distribution, and uses PCA to prune outliers. Remaining peaks represent color counts. This deterministic algorithm bypasses stochastic clustering and deep models, demonstrating efficient execution and resistance to noise (Al-Rawi, 2021).
• Semantic Feature Color Analysis via Clustering and Distance Metrics: Employs a multi-stage pipeline for face/hair/iris/vein color extraction using deep segmentation, X-means clustering in HSV/LAB, and perceptually uniform distance metrics like CIEDE2000 (Delta E) to classify tones, demonstrating high accuracy under variable conditions (Alyoubi et al., 20 May 2025).
• High-order Fabry–Pérot Multispectral Filter Arrays: Utilizes subwavelength cavity structuring and ultra-thin platinum layers for selective resonance suppression in chip-scale multispectral imagers. Achieves narrow linewidths, high transmission, octave-spanning spectral range, and CMOS compatibility, enabling pure color extraction for advanced imaging (Xiang et al., 2023).

2. Color Spaces, Statistical Modeling, and Feature Representation

HCEs typically operate in perceptually uniform color spaces—such as CIE Lab, LCh, or HSV—rather than raw RGB. Key advantages include:

• Uniform Distance Metrics: LAB space enables direct Euclidean computations corresponding to human color perception. For classification, CIEDE2000 (Delta E) employs parametric scaling and rotation terms:

$$\Delta E_{00} = \sqrt{\left(\frac{\Delta L'}{k_L S_L}\right)^2 + \left(\frac{\Delta C'}{k_C S_C}\right)^2 + \left(\frac{\Delta H'}{k_H S_H}\right)^2 + R_T \left(\frac{\Delta C'}{k_C S_C}\right)\left(\frac{\Delta H'}{k_H S_H}\right)}$$

• Gaussian Density Estimation: Models color distributions in Lab as 3D point clouds, estimated via

$$\rho(\mathbf{x}) = \sum_{i=1}^{N} \frac{1}{(2\pi)^{3/2} |\Sigma|^{1/2}} \exp\left(-\frac{1}{2}(\mathbf{x}-\mu_i)^T \Sigma^{-1} (\mathbf{x}-\mu_i)\right)$$

allowing robust color phenotype descriptors and zone comparisons (Li et al., 2019).

• Harmonic Templates and Axes: Palette-based extraction defines color relationships as axes in chroma–hue spaces, facilitating harmonization and alignment operations (Tan et al., 2018).
• Clustering and Binning Approaches: K-means, X-means, channelized binning, and neural methods are employed for color quantization or dominant color extraction, with each approach yielding representative centroids or ordinal color lists.

3. Physical Devices and Color Purity Enhancement

HCE principles extend to engineered color filter arrays for multispectral imaging:

Technology	Feature	Application Domain
High-order Fabry–Pérot MSFA	Narrow FWHM (13–31 nm); octave-wide;	Snapshot multispectral,
with subwavelength structuring, Pt	>60% transmission; CMOS compatible;	remote sensing, display
layer for resonance suppression	spectral tuning via effective index	technologies

By deploying subwavelength mesh/grating for effective index tuning and a central Pt layer for odd-order suppression, such arrays surpass the bandwidth, purity, and size constraints of earlier diffractive filters (Xiang et al., 2023).

4. Practical Applications and Impact

HCE modules and devices underpin a wide spectrum of practical functions:

• Image Retrieval and Indexing: D-CDEN descriptors enhance discrimination and reduce false positives by coupling histogram-based occurrence with spatial entropy (Alamdar et al., 2012).
• Animation Colorization: High-level semantic color extraction via transformer-guided cross-attention in AnimeColor achieves temporal consistency and sketch alignment (Zhang et al., 27 Jul 2025).
• Palette Extraction and Harmonization: Palette-based frameworks rapid decomposition and real-time editing for image/video recoloring and designer GUIs (Tan et al., 2018).
• Art and Sketch Generation: Clustering and GAN pipelines produce colored outlines and sketches with aesthetic scores on par or superior to standard datasets (Manushree et al., 2021).
• Color Quantification in Biology: ColourQuant pipeline enables robust, high-throughput color phenotyping (mean/variance, GDE, TPS deformation) in plant science, generalizable to other domains (Li et al., 2019).
• Multispectral and Remote Sensing: MSFA arrays facilitate compact, broad-band imaging for environmental, metrological, or medical applications (Xiang et al., 2023).
• Biometric Feature Classification: AI-driven pipelines segment, cluster, and classify human feature colors for personalization and recommendation systems (Alyoubi et al., 20 May 2025).
• AI Fashion and Design: Deterministic color counting yields baseline for downstream extraction or recommender models (Al-Rawi, 2021).

5. Quantitative Results and Evaluation

HCEs demonstrate significant efficiency and accuracy metrics in their respective domains:

• Precision/Recall: D-CDEN precision-recall curves show consistent ordinal improvement over I-CDE at multiple recall levels (Alamdar et al., 2012).
• Extraction Error: Channelized binning achieves mean Euclidean error $<1$ in dominant color extraction against ground truth (Algur et al., 2016).
• Scalability: Palette-based 5D decomposition executes 6M-pixel images in 20–30 ms, enabling real-time editing (Tan et al., 2018).
• Deep Colormap Extraction: Achieves DTW reductions of 88–94% compared to palette-based and sequence-preserving methods on synthetic datasets (Yuan et al., 2021).
• NIMA Scores and User Studies: Sketch colorization outputs score comparably or superior in aesthetics/technical quality, validated in targeted artist and layperson studies (Manushree et al., 2021).
• Execution Time: Cumulative histogram color counting reduces execution by two orders of magnitude relative to GMM and DCNN alternatives (Al-Rawi, 2021).
• Classification Accuracy: AI color analysis for human features attains up to 80% tone classification accuracy via Delta E–HSV combined with Gaussian blur (Alyoubi et al., 20 May 2025).
• Optical Purity and Transmission: Multispectral filter arrays maintain FWHM of 13–31 nm and average transmission above 60%, with angular and polarization-insensitive behavior (Xiang et al., 2023).

6. Limitations and Prospects for Future Research

Key challenges remain:

• Subjectivity in Color Counting: Human perception of color counts may not align with algorithmic estimates, necessitating flexible tuning and robust outlier handling (Al-Rawi, 2021).
• Imaging Noise and Artifacts: Color extraction can be compromised by compression, distortion, or uneven illumination, motivating advanced noise models and adaptive thresholds.
• Integration of Features: A plausible implication is that joint modeling of color with texture, shape, or spatial cues will drive progress in retrieval, classification, and generative synthesis (Alamdar et al., 2012).
• Scalability and Speed: Research into GPU acceleration or real-time filter design is ongoing for deployment in demanding or interactive scenarios (Al-Rawi, 2021, Xiang et al., 2023).
• Domain Adaptation: Further validation across diverse datasets, domains (biomedicine, industry, art), and visual complexity is required for robust HCE generalization (Alamdar et al., 2012, Li et al., 2019).
• Active Tunability and Hybrid Models: Exploring dynamic, tunable filters for physical extractors, and hybrid statistical/learning-based approaches for software modules represents important future directions (Xiang et al., 2023, Al-Rawi, 2021).

In summary, High-level Color Extractors (HCEs) encompass a diverse, technically sophisticated set of frameworks, devices, and algorithms for color modeling, feature extraction, and semantic guidance in visual processing. Their ongoing refinement and integration into practical systems promise significant impact across imaging science, multimedia, art, design, and digital personalization.