- The paper demonstrates that employing the RGB color space in the SLIC model improves image quality, achieving BD-BR gains of up to 17.96% on CIEDE2000 metrics.
- The paper shows that separate luminance and chrominance processing in YUV and LAB models can enhance compression efficiency, despite adding complexity.
- The paper compares SLIC variants with state-of-the-art codecs, confirming that while SLIC-RGB excels in structure and color fidelity, it demands higher computational resources.
Effect of Color Spaces in Learned Image Compression
In "A Study on the Effect of Color Spaces in Learned Image Compression," Srivatsa Prativadibhayankaram et al. investigate the influence of different color spaces on learned image compression models. Employing the Structure And Color-based Learned Image Codec (SLIC) from their prior work, the authors examine the performance of this codec across YUV, LAB, and RGB color spaces. The research provides a comprehensive evaluation of these color spaces on various datasets, comparing them to state-of-the-art image codecs.
Methodology and Model Architecture
The paper's central focus is the SLIC model, which is evaluated in three color spaces:
- SLIC-YUV: The original model working within the YUV color space.
- SLIC-LAB: A new variant adapted to the LAB color space.
- SLIC-RGB: An RGB version where all three channels are processed in a single branch.
The architectures for YUV and LAB models share a two-branch structure, separating luminance and chrominance processing for better efficiency. Conversely, the RGB model employs a single-branch structure, aligning with common RGB processing methodologies in learned codecs.
The SLIC-YUV and SLIC-LAB models process luminance and chrominance data independently, potentially optimizing structural and color data differently. However, this separation increases the model complexity. The RGB model, although more straightforward in architectural design, encompasses all three color channels in a single processing branch, resulting in a more complex model.
Loss Function
The loss function integrates three distortion metrics: Mean Squared Error (MSE), Multi-Scale Structural Similarity Index Measure (MS-SSIM), and CIEDE2000 color difference metric (ΔE2000). These metrics are balanced by Lagrangian multipliers to optimize both structural fidelity and color accuracy effectively. This approach ensures the compression model optimizes for comprehensive image quality, not merely minimizing file size.
Experimental Results
The models were rigorously tested across several datasets, with a particular focus on the Kodak dataset for evaluating rate-distortion performance.
Findings indicate:
- SLIC-RGB performs superiorly with a BD-BR gain of 13.14% (MS-SSIM) and 17.96% (CIEDE2000) but at a higher computational cost.
- SLIC-YUV and SLIC-LAB show competitive performance in terms of PSNR and MS-SSIM but do not match the RGB model's color accuracy.
The paper compares SLIC variants against established codecs like Cheng2020, ELIC, and JPEG AI. Despite SLIC-RGB's higher complexity, its performance markedly surpasses other variants in terms of MS-SSIM and CIEDE2000 metrics, showcasing the RGB model's advantage in balancing structural and color fidelity.
Implications of Color Spaces
The impulse response analysis elucidates that:
- YUV and LAB models effectively manage structural and color features through their independent luminance and chrominance processing branches.
- RGB model captures a blend of structural and color features within a unified processing framework, indicating implicit learning of color space nuances without explicit separation.
Practical and Theoretical Implications
From a practical standpoint, the SLIC-RGB model demonstrates higher performance potential for applications requiring high color fidelity and structural accuracy. However, the increased complexity of the RGB variant may limit its practicality in resource-constrained environments. Theoretically, the research underlines the benefit of evaluating color spaces in learned image compression—a relatively unexplored area.
Future Directions
Future research may explore:
- Optimization techniques to balance the computational overhead of the RGB model.
- Exploration of other color spaces such as HSV or XYZ.
- Potential adaptations for video compression codecs using similar methodologies.
Conclusion
The paper reinforces the importance of color spaces in image compression efficacy. The SLIC-RGB model, despite its higher complexity, provides superior compression performance retaining structural and color fidelity. The YUV and LAB models offer a compelling alternative for scenarios prioritizing computational efficiency. This research sets a cornerstone for future explorations into optimizing learned image codecs across diverse color spaces.
