Learning from the CNN-based Compressed Domain

Abstract

Images are transmitted or stored in their compressed form and most of the AI tasks are performed from the re-constructed domain. Convolutional neural network (CNN)-based image compression and reconstruction is growing rapidly and it achieves or surpasses the state-of-the-art heuristic image compression methods, such as JPEG or BPG. A major limitation of the application of the CNN-based image compression is on the computation complexity during compression and reconstruction. Therefore, learning from the compressed domain is desirable to avoid the computation and latency caused by reconstruction. In this paper, we show that learning from the compressed domain can achieve comparative or even better accuracy than from the reconstructed domain. At a high compression rate of 0.098 bpp, for example, the proposed compression-learning system has over 3% absolute accuracy boost over the traditional compression-reconstruction-learning flow. The improvement is achieved by optimizing the compression-learning system targeting original-sized instead of standardized (e.g., 224x224) images, which is crucial in practice since real-world images into the system have different sizes. We also propose an efficient model-free entropy estimation method and a criterion to learn from a selected subset of features in the compressed domain to further re-duce the transmission and computation cost without accuracy degradation.