Abstract-Multiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source description and decode some fraction of that string. Decoding a small portion of the binary string gives a low-resolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms for designing fixed-and variable-rate multiresolution vector quantizers. Experiments on synthetic data demonstrate performance close to the theoretical performance limit. Experiments on natural images demonstrate performance improvements of up to 8 dB over tree-structured vector quantizers. Some of the lessons learned through multiresolution vector quantizer design lend insight into the design of more sophisticated multiresolution codes.Index Terms-Embedded source code design, fixed rate, multiuser, network, progressive transmission, successive refinement, variable rate.

We introduce a new algorithm for progressive or multiresolution image compression. The algorithm improves on the Set Partitioning in Hierarchical Trees (SPIlT) algorithm by replacing the SPIlT encoder. The new encoder optimizes the multiresolution code performance relative to a user-defined probability distribution (or priority function) over the code's rates or resolutions. The new algorithm's decoder is identical to the SPIlT decoder. The resulting code achieves the optimal expected performance across resolutions subject to the constraints imposed by the use of the SPIHT decoder and the distribution (or priorities) over resolutions set by the user. The encoder optimization yields performance improvements at the rates or resolutions of greatest importance (according to the encoder's priority function) at the expense of performance degradation at low priority rates or resolutions. The algorithm is fully compatible at the decoder with the original SPIlT algorithm. In particular, the decoder requires no knowledge of the priority function employed at the encoder. Experimental results on an image containing both text and photographic material yield up to 0.86 dB performance improvement over SPIHT at the resolution of highest priority.

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