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Variable-precision arithmetic for vector quantization (1994).
| Content Provider | CiteSeerX |
|---|---|
| Author | Dionysian, Raffi |
| Abstract | This research proposes and investigates a method for the storage and computation in Vector Quantization (VQ) -- a promising technique for image/speech compression. The improvement is in the representation and arithmetic algorithm; the idea is independent of the technology and accommodates different search algorithms. Specifically, with simple lossless compression, the codebook storage in tree searched VQ is reduced more than 20%. For large codebooks, the simulations predict that the compression would be more than 40%. The compression of codevectors is achieved with Variable-Precision Representation (VPR), where we eliminate the sign extension bits. By categorizing vectors, VPR uses non-stationary nature of codevectors. Entropy measure shows that VPR compresses at least 75% as well as Huffman coding of vector elements. In conjunction |
| File Format | |
| Publisher Date | 1994-01-01 |
| Access Restriction | Open |
| Subject Keyword | Vector Quantization Variable-precision Arithmetic Codebook Storage Variable-precision Representation Accommodates Different Search Algorithm Simple Lossless Compression Vector Element Large Codebooks Arithmetic Algorithm Non-stationary Nature Huffman Coding Entropy Measure Image Speech Compression Promising Technique Sign Extension Bit |
| Content Type | Text |
