[J20] Scalable Color Quantization for Task-Centric Image Compression

Abstract

Conventional image compression techniques targeted for the perceptual quality are not generally optimized for classification tasks using deep neural networks (DNNs). To compress images for DNN inference tasks, recent studies have proposed task-centric image compression methods with quantization techniques optimized for DNN inference. Among them, color quantization was proposed to reduce the amount of data per pixel by limiting the number of distinct colors (color space) in an image. However, quantizing images into various color space sizes requires training and inference of multiple DNNs, each of which is dedicated to each color space. To overcome this limitation, we propose a scalable color quantization method, where images with variable color space sizes can be extracted from a master image generated by a single DNN model. This scalability is enabled by weighted color grouping which constructs a color palette using critical color components for the classification task. We also propose an adaptive training method that can jointly optimize images with various color-space sizes. The results show that the proposed method supports dynamic changes of the color space size between 1-6 bit color space per pixel, while even increasing the inference accuracy at a low bit precision up to 20.2% and 46.6% compared to other task- and human-centric color quantizations, respectively.