The well-known error propagation problem inherent in any variable-length coding operation limits the usefulness of variable-length encoded scalar quantizers for transmission over noisy channels. In the absence of chan...
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The well-known error propagation problem inherent in any variable-length coding operation limits the usefulness of variable-length encoded scalar quantizers for transmission over noisy channels. In the absence of channel noise however, these quantizers are known to perform better than error-minimizing fixed-rate Lloyd-Max quantizers for a wide class of memoryless sources. Motivated by this observation, a low complexity fixed-rate structured vector quantizer for memoryless sources is described. This quantizer is referred to as the scalar-vector quantizer and the structure of its codebook is derived from a variable-length scalar quantizer. Design and implementation algorithms for this quantizer are developed and bounds on its performance are provided. The scalar-vector quantizer can be designed and implemented even for fine (high rate) quantization at relatively large block lengths and can achieve a rate-distortion performance superior to that of implementable LBG vector quantizers. Simulation results show that performance close to that of the optimal entropy-constrained scalar quantizer is possible with this fixed-rate quantizer. The scalar-vector quantizer is also robust against channel errors and outperforms both Lloyd-Max and entropy-constrained scalar quantizers for a wide range of channel error probabilities. These ideas are extended (in Part II) to the quantization of vector sources and, consequently, to sources with memory.
In this paper, we give a new variable-rate and variable-distortion coding scheme and show a coding theorem for time-discrete stationary sources with abstract alphabets and a single-letter fidelity criterion without as...
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In this paper, we give a new variable-rate and variable-distortion coding scheme and show a coding theorem for time-discrete stationary sources with abstract alphabets and a single-letter fidelity criterion without assuming reference letters. The approach used to prove the coding theorem is then specialized to maximum-distortion coding and to fixed-rate coding and the corresponding coding theorems are proved for stationary sources. We also consider the possibility of extending the results to time-continuous sources. We still need a reference letter for fixed-rate coding.
A new coding scheme based on the scalar-vector quantizer (SVQ) is developed for compression of medical images. SVQ is a fixed-rate encoder and its rate-distortion performance is close to that of optimal entropy-constr...
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ISBN:
(纸本)0819419699
A new coding scheme based on the scalar-vector quantizer (SVQ) is developed for compression of medical images. SVQ is a fixed-rate encoder and its rate-distortion performance is close to that of optimal entropy-constrained scalar quantizers (ECSQs) for memoryless sources. The use of a fixed-rate quantizer is expected to eliminate some of the complexity issues of using variable-length scalar quantizers. When transmission of images over noisy channels is considered, our coding scheme does not suffer from error propagation which is typical of coding schemes which use variable-length codes. For a set of magnetic resonance (MR) images, coding results obtained from SVQ and ECSQ at low bit-rates are indistinguishable. Furthermore, our encoded images are perceptually indistinguishable from the original, when displayed on a monitor. This makes our SVQ based coder an attractive compression scheme for picture archiving and communication systems (PACS), currently under consideration for an all digital radiology environment in hospitals, where reliable transmission, storage, and high fidelity reconstruction of images are desired.
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