Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression
This addresses storage challenges for deploying deep learning in resource-constrained environments, offering a scalable solution, though it appears incremental as it builds on color-space compression ideas.
The paper tackles the problem of high storage demands in large-scale image datasets by proposing Dataset Color Quantization (DCQ), a framework that compresses datasets by reducing color-space redundancy while preserving information for training, resulting in significant improvements in training performance under aggressive compression across multiple datasets like CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-1K.
Large-scale image datasets are fundamental to deep learning, but their high storage demands pose challenges for deployment in resource-constrained environments. While existing approaches reduce dataset size by discarding samples, they often ignore the significant redundancy within each image -- particularly in the color space. To address this, we propose Dataset Color Quantization (DCQ), a unified framework that compresses visual datasets by reducing color-space redundancy while preserving information crucial for model training. DCQ achieves this by enforcing consistent palette representations across similar images, selectively retaining semantically important colors guided by model perception, and maintaining structural details necessary for effective feature learning. Extensive experiments across CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-1K show that DCQ significantly improves training performance under aggressive compression, offering a scalable and robust solution for dataset-level storage reduction. Code is available at \href{https://github.com/he-y/Dataset-Color-Quantization}{https://github.com/he-y/Dataset-Color-Quantization}.