Weighted universal image compression

We describe a general, coding strategy leading to a family of universal ima ge compression systems designed to give good performance in applications wh ere the statistics of the source to be compressed are not available at desi gn time or vary over time or space. The basic approach considered uses a tw o-stage structure in which the single source code of traditional image comp ression systems is replaced with a family of codes designed to cover a larg e class of possible sources. To illustrate this approach, we consider the o ptimal design and use of two-stage codes containing collections of vector q uantizers (weighted universal vector quantization), bit allocations for JPE G-style coding (weighted universal bit allocation), and transform codes (we ighted 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 quantizatio n by over 9 dB, On the same data sequence, weighted universal bit allocatio n outperforms a JPEG-style code by over 2.5 dB. On a collection of mixed te xt and image data, weighted universal transform coding outperforms a single , data-optimized transform code (which gives performance almost identical t o that of JPEG) by over 6 dB.