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Quantization of Multiple Sources Using Integer Bit Allocation
Snowbird, Utah March 29-March 31
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/DCC.2005.76Data Compression Conference (DCC'05)
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Benjamin Farber, Fair Isaac Corp., San Diego, CA
Kenneth Zeger, University of California, San Diego
Asymptotically optimal bit allocation among a set of quantizers for a finite collection of sources was determined in 1963 by Huang and Schultheiss. Their solution, however, gives a real-valued bit allocation, whereas in practice, integer-valued bit allocations are needed. We compare the performance of the Huang-Schultheiss solution to that of an optimal integer-valued bit allocation. Specifically, we derive upper and lower bounds on the deviation of the mean squared error using optimal integer-valued bit allocation from the mean squared error using optimal real-valued bit allocation. One consequence shown is that optimal integer-valued bit allocations do not necessarily achieve the same performance as that predicted by Huang-Schultheiss, for asymptotically large transmission rates. We also prove that integer bit allocation vectors that minimize the Euclidean distance to the optimal real-valued bit allocation vector are optimal integer bit allocations.
Citation:
Benjamin Farber, Kenneth Zeger, "Quantization of Multiple Sources Using Integer Bit Allocation," dcc, pp.368-377, Data Compression Conference (DCC'05), 2005
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