JPEG Compliant Compression for DNN Vision

Conventional image compression techniques are primarily developed for the human visual system. However, with the extensive use of deep neural networks (DNNs) for computer vision, more and more images will be consumed by DNN-based intelligent machines, which makes it crucial to develop image compression techniques customized for DNN vision while being JPEG compliant. In this paper, we revisit the JPEG rate distortion theory for DNN vision. First, we propose a novel distortion measure, dubbed the sensitivity weighted error (SWE), for DNN vision. Second, we incorporate SWE into the soft decision quantization (SDQ) process of JPEG to trade SWE for rate. Finally, we develop an algorithm, called OptS, for designing optimal quantization tables for the luminance channel and chrominance channels, respectively. To test the performance of the resulting DNN-oriented compression framework and algorithm, experiments of image classification are conducted on the ImageNet dataset for four prevalent DNN models. Results demonstrate that our proposed framework and algorithm achieve better rate-accuracy (R-A) performance than the default JPEG. For some DNN models, our proposed framework and algorithm provide a significant reduction in the compression rate up to 67.84% with no accuracy loss compared to the default JPEG.

I. Introduction

Image compression is one of the fundamental domains in image processing and computer vision (CV), which has been well developed in the past decades. Among all image compression approaches, JPEG [1] has become the defacto one, considering its extensive application. JPEG is a lossy compression technique developed for the human visual system, dubbed human-oriented compression (HOC) in this paper. HOC algorithms can aggressively compress an image as they allow information loss during compression which is imperceptible for humans.