Multiple-rate structured vector quantization of image pyramids

Mahesh, Balakrishnan; Pearlman, William A.

doi:10.1016/1047-3203(91)90001-v

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…We next de ne a rate decrement, denoted by R j+1 ab as the decrease in entropy of quantizer Q j+1 (a; b) relative to the entropy of quantizer Q j by R j+1 ab = H Q j ? H Q j+1 (19) or,…”

Section: Constrained Pairwise Nearest Neighbor Quantizer Designmentioning

confidence: 99%

A clustering algorithm for entropy-constrained vector quantizer design with applications in coding image pyramids

Garrido

Pearlman²,

Finamore³

1995

IEEE Trans. Circuits Syst. Video Technol.

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{A clustering algorithm for the design of e cient vector quantizers to be followed by entropy coding is proposed. The algorithm, called entropy-constrained pairwise nearest neighbor (ECPNN), designs codebooks by merging the pair of Voronoi regions which gives the least increase in distortion for a given decrease in entropy. The algorithm is an entropy-constrained version of the pairwise nearest neighbor (PNN) clustering algorithm of Equitz and can be used as an alternative to the entropyconstrained vector quantizer design (ECVQ) proposed by Chou, Lookabaugh and Gray. By a natural extension of the ECPNN algorithm we develop another algorithm that designs alphabet-and entropy-constrained vector quantizers and call it alphabet-and entropy-constrained pairwise nearest neighbor (AECPNN) design. The AECPNN algorithm can be used as alternative to the alphabet-and entropy-constrained vector quantizer design (AECVQ) proposed by Rao and Pearlman, that is directly based on the ECVQ design algorithm. Through simulations on synthetic sources, we show that ECPNN and ECVQ have indistinguishable mean-square-error versus rate performance and that the ECPNN and AECPNN algorithms obtain as close performance by the same measure as the ECVQ and AECVQ algorithms. The advantages over

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“…We next de ne a rate decrement, denoted by R j+1 ab as the decrease in entropy of quantizer Q j+1 (a; b) relative to the entropy of quantizer Q j by R j+1 ab = H Q j ? H Q j+1 (19) or,…”

Section: Constrained Pairwise Nearest Neighbor Quantizer Designmentioning

confidence: 99%

A clustering algorithm for entropy-constrained vector quantizer design with applications in coding image pyramids

Garrido

Pearlman²,

Finamore³

1995

IEEE Trans. Circuits Syst. Video Technol.

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“…Furthermore the TSVQ to be used has to be multi-rate for dynamical channel requirements and rate allocation to frequency bands. For this reason an unbalanced tree structured vector quantizer which can be grown incrementally in the rate distortion sense (node by node) has been selected for our purposes (see [10], [12]). …”

Section: Spatial Domain Block Encodingmentioning

confidence: 99%

“…In [9] and [12] applications of this rate allocation algorithm to the frequency bands of a pyramidal structure have been demonstrated. The rate allocation approach to be implemented here is similar to that of [12].…”

Section: Introductionmentioning

confidence: 97%

“…An efficient rate allocation algorithm has been derived in [1 1] based on the preliminary work on regression and classification trees in [15]. In [9] and [12] applications of this rate allocation algorithm to the frequency bands of a pyramidal structure have been demonstrated. The rate allocation approach to be implemented here is similar to that of [12].…”

Section: Introductionmentioning

confidence: 99%

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<title>Hierarchical image sequence coding with tree-structured vector quantization</title>

Bayazıt

Pearlman

1993

SPIE Proceedings

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The paper presents two different approaches to image sequence coding which exploit the spatial frequency statistics as well as the spatial and temporal correlation present in the video signal. The first approach is the pyramidal decomposition of the Motion Compensated Frame Difference (MCFD) signal in the frequency domain and the subsequent coding by unbalanced Tree Structured Vector Quantizers (TSVQ) designed to match the statistics of the frequency bands. The type of TSVQ used in this study possesses the advantage of low computational complexity with coding performance comparable to full-search vector quantization. The second approach is similar except that the order of motion estimation/compensation and pyramidal decomposition are interchanged. Motion estimation on each frequency band and on only the low-pass frequency band of each level of the hierarchy are both considered and compared. In this approach the low computational complexity of the block encoder is enhanced by the fact that processing of frequency bands can be independently implemented in parallel. Both approaches make use of the BFOS algorithm for rate allocation to the frequency bands. Miss America image sequence has been coded at an average PSNR of 39.17dB and an average rate of O.35bpp with the first approach. Implementation of the second approach resulted in an average PSNR of 38.70dB at an average rate of O.28bpp. Both approaches are suitable for multi-rate video conference applications.

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“…All paths are examined in turn and the process is repeated until the desired rate is reached. For further explanation, see [38] and [40]. The rate-distortion point is guaranteed to lie on the convex hull.…”

Section: Rate Allocation To Subbandsmentioning

confidence: 99%

Information Theory and Image Coding

Pearlman

1995

Handbook of Visual Communications

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Multiple-rate structured vector quantization of image pyramids

Cited by 11 publications

References 12 publications

A clustering algorithm for entropy-constrained vector quantizer design with applications in coding image pyramids

A clustering algorithm for entropy-constrained vector quantizer design with applications in coding image pyramids

<title>Hierarchical image sequence coding with tree-structured vector quantization</title>

Information Theory and Image Coding

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