New finite state vector quantizers for images

Kim, T.

doi:10.1109/icassp.1988.196809

Cited by 40 publications

(3 citation statements)

References 9 publications

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“…OMVQ is similar to SMVQ but the blocks to be encoded are "overlap-matched" and the states are determined from the overlapped regions. Results 70,69 show an improvement in PSNR of 1-3 dB over a standard VQ compression scheme for several images. However, the perceptual quality of the images using SMVQ and OMVQ shows a dramatic improvement in image quality.…”

Section: Vector Quantizationmentioning

confidence: 94%

Image and Video Compression: A Review

Podilchuk

Safranek

1997

Selected Topics in Electronics and Systems

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The area of image and video compression has made tremendous progress over the last several decades. The successes in image compression are due to advances and better understanding of waveform coding methods which take advantage of the signal statistics, perceptual methods which take advantage of psychovisual properties of the human visual system (HVS) and object-based models especially for very low bit rate work. Recent years have produced several image coding standards-JPEG for still image compression and H.261, MPEG-I and MPEG-II for video compression. While we have devoted a special section in this paper to cover international coding standards because of their practical value, we have also covered a large class of nonstandard coding technology in the interest of completeness and potential future value. Very low bit rate video coding remains a challenging problem as does our understanding of the human visual system for perceptually optimum compression.The wide range of applications and bit rates, from video telephony at rates as low as 9.6 kbps to HDTV at 20 Mbps and higher, has acted as a catalyst for generating new ideas in tackling the different challenges characterized by the particular application. The area of image compression will remain an interesting and fruitful area of research as we focus on combining source coding with channel coding and multimedia networking.

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Section: Vector Quantizationmentioning

confidence: 94%

Image and Video Compression: A Review

Podilchuk

Safranek

1997

Selected Topics in Electronics and Systems

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“…Unlike linear prediction-based quantizers, trellis quantizers can potentially use non-linear prediction realizable with a finitestate quantizer. For example, finite-state quantizers have been shown to be effective in image coding [7] and video coding [8].…”

Section: Introductionmentioning

confidence: 99%

CTH04-2: A Multiple-Description Trellis Quantizer for Sources With Memory

Yahampath

2006

IEEE Globecom 2006

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An approach to multiple description block quantization of a correlated source for transmission over a lossy communication channel is presented. The main idea is to optimally control the redundancy due to both the quantizer index assignment and the residual temporal correlation within a block of quantizer outputs. This is achieved by using a trellis encoder in which each state has an associated index assignment, a finite-state central decoder, and a set of Markov-model based side-decoders which exploit the encoder residual redundancy to reconstruct the source based on an incomplete set of descriptions. An iterative descent algorithm is presented for optimizing the proposed coding system for source statistics and channel loss probability. Simulation results are given which demonstrate the performance of trellis quantizers designed for a Gauss-Markov source.

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“…Although many kinds of applications with FSVQ have been proposed, such as speech coding system [1], image coding system [2] [3], it seldom uses in practical system because of its sensitivity to channel errors.…”

Section: Introductionmentioning

confidence: 99%

Entropy coded vector quantization with hidden Markov models

Yonezaki

Shikano²

Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96

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We propose a new vector quantization approach, which consists of Hidden Markov Models(HMMs) and entropy coding scheme. The entropy coding system is determined depending on the speech status modeled by HMMs, so the proposing approach can adaptively allocate suitable numbers of bits to the codewords. This approach realizes about 0.3[dB] coding gain in cepstrum distance(8 states HMMs). In other words, 8 bit-codebook is represented by about 6.5 bits for average code length. We also research for robustness to the channel error. HMMs and the entropy coding system, which seem to be weak to the channel error, are augmented to be robust, so that the inuence of the channel error is decreased into one-third.

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New finite state vector quantizers for images

Cited by 40 publications

References 9 publications

Image and Video Compression: A Review

Image and Video Compression: A Review

CTH04-2: A Multiple-Description Trellis Quantizer for Sources With Memory

Entropy coded vector quantization with hidden Markov models

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