Learned Progressive Image Compression With Dead-Zone Quantizers

Abstract

Progressive coding is essential to the practical deployment of learned image compression over heterogeneous networks and clients. Existing methods for learned progressive image compression require complex and empirical design to achieve near-optimal rate-distortion performance over a wide range of bit-rates. However, these methods are limited by the implicit learned mechanism based on neural networks and introduction of uniform quantizers. In this paper, we propose generalized learned progressive image compression with analytic rate-distortion optimization using dead-zone quantizers on the latent representation. Specifically, we reveal that dead-zone quantizers, as a general case of uniform quantizers, are equivalent to uniform quantizers in fixed-rate nonlinear transform coding and can prevent extra redundancy in embedded quantization for progressive coding. Consequently, we propose rate-distortion optimized learned progressive coding by approximating the optimal quantizer in the source spaces using dead-zone quantizers in an analytic manner on the Laplacian source. To our best knowledge, this paper is the first to achieve general learned progressive coding from the perspective of optimal quantizers. The proposed method achieves theoretically sound and practically efficient embedded quantization and learned progressive coding of latent representations with improved rate-distortion performance. It can also enable embedded quantization with diverse assignments of truncation points and support flexible configuration of quality layers of varying numbers and at varying target bit-rates. Furthermore, we successfully incorporate the proposed method into existing pre-trained fixed-rate models to realize progressive learned image compression without re-training. Experimental results demonstrate that the proposed method achieves state-of-the-art rate-distortion performance in learned progressive image compression compared with traditional codecs and recent learned methods.