A Novel Deep Progressive Image Compression Framework

Abstract

In Internet applications, compressing the image without perceptually distinguishable distortions and loading the images without notable delays in the client end can significantly improve the user experience. Compressing the image at high bit rates can maintain the high quality of the decoded image but in cost of long transmitting and decoding time, resulting in bad user experience. The progressive coding scheme can resolve the conflict between the high quality requirement and the large loading delay. This paper proposes a novel efficient progressive image coding framework based on deep convolutional neural networks. The proposed framework is composed of a uniform encoder network and two progressive decoder networks. The encoder network decomposes the input image into two scales of representations, that can be transmitted and reconstructed progressively into a basic quality preview image and a high-quality image by two individual decoder networks respectively. All the networks are jointly learned when achieving the rate distortion optimization of both scales. Experiments results show that the proposed method has much better coding performance than the commercial codecs WebP and JPEG, which are commonly used in Internet applications. Meanwhile, the proposed codec consumes much less time to load the image compared with WebP.