Weighted universal image compression

作     者：Effros, M Chou, PA Gray, RM 

作者机构：CALTECH Dept Elect Engn Pasadena CA 91125 USA Microsoft Corp Redmond WA 98052 USA Stanford Univ Dept Elect Engn Informat Syst Lab Stanford CA 94305 USA 

出 版 物：《IEEE TRANSACTIONS ON IMAGE PROCESSING》 (IEEE Trans Image Process)

年 卷 期：1999年第8卷第10期

页      面：1317-1329页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Center for Telecommunications Stanford University, SU UK Research and Innovation, UKRI, (98052) National Science Foundation, NSF, (MIP-9501977) 

主　　题：adaptive coding bit allocation clustering image compression JPEG perceptual distortion measures transform coding two-stage coding universal coding vector quantization 

摘      要：We describe a general, coding strategy leading to a family of universal image compression systems designed to give good performance in applications where the statistics of the source to be compressed are not available at design time or vary over time or space. The basic approach considered uses a two-stage structure in which the single source code of traditional image compression systems is replaced with a family of codes designed to cover a large class of possible sources. To illustrate this approach, we consider the optimal design and use of two-stage codes containing collections of vector quantizers (weighted universal vector quantization), bit allocations for JPEG-style coding (weighted universal bit allocation), and transform codes (weighted universal transform coding). Further, we demonstrate the benefits to be gained from the inclusion of perceptual distortion measures and optimal parsing. The strategy yields two-stage codes that significantly outperform their single-stage predecessors. On a sequence of medical images, weighted universal vector quantization outperforms entropy coded vector quantization by over 9 dB, On the same data sequence, weighted universal bit allocation outperforms a JPEG-style code by over 2.5 dB. On a collection of mixed text and image data, weighted universal transform coding outperforms a single, data-optimized transform code (which gives performance almost identical to that of JPEG) by over 6 dB.