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Data Compression Conference (DCC '04)   p. 22
Multi-resolution Source Coding Using Entropy Constrained Dithered Scalar Quantization
Qian Zhao, Oracle Corp., Redwood City, CA
Hanying Feng, Stanford University, CA
Michelle Effros, California Institute of Technology, Pasadena, CA

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/DCC.2004.1281447
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Abstract
In this paper, we build multi-resolution source codes using entropy constrained dithered scalar quantizers. We demonstrate that for n-dimensional random vectors, dithering followed by uniform scalar quantization and then by entropy coding achieves performance close to the n-dimensional optimum for a multi-resolution source code. Based on this result, we propose a practical code design algorithm and compare its performance with that of the Set Partitioning in Hierarchical Trees (SPIHT) algorithm on natural images.
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Citation:  Qian Zhao, Hanying Feng, Michelle Effros, "Multi-resolution Source Coding Using Entropy Constrained Dithered Scalar Quantization," dcc, p. 22,  Data Compression Conference (DCC '04),  2004