
An Upper Limit of AC Huffman Code Length in JPEG Compression
A strategy for computing upper codelength limits of AC Huffman codes fo...
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Finitelength performance comparison of network codes using random vs Pascal matrices
In this letter, we evaluate the finitelength performance of network cod...
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MultipleRate Channel Codes in GF(p^n^2)
A code C(n, k, d) defined over GF(q^n) is conventionally designed to enc...
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Cumulant Generating Function of Codeword Lengths in VariableLength Lossy Compression Allowing Positive Excess Distortion Probability
This paper considers the problem of variablelength lossy source coding....
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Globehopping
We consider versions of the grasshopper problem (Goulko and Kent, 2017) ...
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Universal almost optimal compression and SlepianWolf coding in probabilistic polynomial time
In a lossless compression system with target lengths, a compressor C map...
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An information theoretic model for summarization, and some basic results
A basic information theoretic model for summarization is formulated. Her...
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RunLength Encoding in a Finite Universe
Text compression schemes and compact data structures usually combine sophisticated probability models with basic coding methods whose average codeword length closely match the entropy of known distributions. In the frequent case where basic coding represents runlengths of outcomes that have probability p, i.e. the geometric distribution (i)=p^i(1p), a Golomb code is an optimal instantaneous code, which has the additional advantage that codewords can be computed using only an integer parameter calculated from p, without need for a large or sophisticated data structure. Golomb coding does not, however, gracefully handle the case where runlengths are bounded by a known integer n. In this case, codewords allocated for the case i>n are wasted. While negligible for large n, this makes Golomb coding unattractive in situations where n is recurrently small, e.g., when representing many short lists of integers drawn from limited ranges, or when the range of n is narrowed down by a recursive algorithm. We address the problem of choosing a code for this case, considering efficiency from both informationtheoretic and computational perspectives, and arrive at a simple code that allows computing a codeword using only O(1) simple computer operations and O(1) machine words. We demonstrate experimentally that the resulting representation length is very close (equal in a majority of tested cases) to the optimal Huffman code, to the extent that the expected difference is practically negligible. We describe efficient branchfree implementation of encoding and decoding.
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