Methods for Designing Differential Quantizers Based on Subjective Evaluations of Edge Busyness

Candy, James C.; Bosworth, R. H.

doi:10.1002/j.1538-7305.1972.tb02666.x

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Chapter 3 Quantization O F Segmented Im Ages1

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1973

1998

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Cited by 32 publications

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“…Other quantizers have been proposed which exploit HVS properties [29,38,97,98]. For example, quantizers which incorporate HVS properties have been proposed for use in quantization of normal (not segmented) gray level images [99][100][101][102] , in differential pulse code modulation (DPCM) [103][104][105][106][107][108][109] and transform coding [21,22,24,[110][111][112].…”

Section: Chapter 3 Quantization O F Segmented Im Agesmentioning

confidence: 99%

Image Segmentation Using Human Visual System Properties With Applications In Image Compression

1989

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Section: Chapter 3 Quantization O F Segmented Im Agesmentioning

confidence: 99%

Image Segmentation Using Human Visual System Properties With Applications In Image Compression

1989

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Picture Coding: The Use of a Viewer Model in Source Encoding*

Limb¹

1973

Bell System Technical Journal

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A method is suggested for inserting viewer criteria directly into coding algorithms; any complex visual model may be used. The technique is applied to a DPCM‐type coder, and a number of variations are compared on the basis of entropy, quality, and complexity. It is found that, using a simple one‐dimensional filter model, the first‐order entropy of the DPCM signal can be reduced by 30 percent for a high‐detail picture with only a small reduction in picture quality. Furthermore, by means of a single threshold control, one can efficiently trade off bit‐rate and picture quality over a large range for use in adaptive strategies.

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Optimum Quantizer Design Using a Fixed-Point Algorithm

Netravali¹,

Saigal²

1976

Bell System Technical Journal

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A fixed‐point algorithm has been used to obtain the parameters (i.e., decision and representative levels) of an “optimum” quantizer that minimizes a quite general distortion measure, subject to an entropy constraint on its output. Construction of the algorithm starts with a point‐to‐set mapping whose fixed point satisfies the well‐known Karush‐Kuhn‐Tucker conditions necessary for a local extremum. A computer program is then used to determine a fixed point of this mapping. Several examples are solved, and correspondence with the existing results in the literature is pointed out. Finally, as conjectured, the growth of the computations as a function of dimensionality n (n: number of representative levels) is found to be of the form a· nb where a is a positive constant and 1.5 ≦ b ≦ 2.0.

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Methods for Designing Differential Quantizers Based on Subjective Evaluations of Edge Busyness

Cited by 32 publications

References 14 publications

Image Segmentation Using Human Visual System Properties With Applications In Image Compression

Image Segmentation Using Human Visual System Properties With Applications In Image Compression

Picture Coding: The Use of a Viewer Model in Source Encoding*

Optimum Quantizer Design Using a Fixed-Point Algorithm

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