A comparison of vector quantization techniques in transform and subband coding of imagery

Blain, M.E.; Fischer, Thomas R.

doi:10.1016/0923-5965(91)90032-w

Cited by 27 publications

(2 citation statements)

References 32 publications

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“…The simulation results show that Out_in clustering algorithm based on density converges quickly. Clustering algorithm is widely used in vector quantization image compression [12][13][14][15][16], Out_in clustering algorithm based on density is same. Q. E. D. Theorem 1 gives a meaningful result about mathematical expectation.…”

Section: Introductionmentioning

confidence: 99%

Clustering Algorithm Based on Density of Data

2021

E3S Web Conf.

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The k_means clustering algorithm has very extensive application. The paper gives out_in clustering algorithm based on density. The algorithm combines distance with data density to adapt to data distribution. It can effectively solve the clustering of data. Out_in clustering based on density reduce distorition by move out and move in. Simulation results show that out_in clustering algorithm is more effective than the k_means clustering algorithm.

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Section: Introductionmentioning

confidence: 99%

Clustering Algorithm Based on Density of Data

2021

E3S Web Conf.

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“…For the monochrome "Lena" image, our PSNR value of 33.86 dB is below that of the adaptive DCT-based scheme of [ll] (34.6 dB), but above those of the adaptive subband coding scheme of [12] (32.5 dB) and the pyramid VQbased DCT scheme of [13] (32.2 dB). Similarly, for the monochrome "Girl" image, we find that the entropyconstrained TCQ system performs better than the lattice VQ-based DCT scheme of [14] (38.8 dB) and the pyramid VQ-based DCT schemes of [15][16] (35.72 dB and 35.92 dB, respectively).…”

Section: Resultsmentioning

confidence: 87%

Transform coding of monochrome and color images using trellis coded quantization

Tong

Marcellin

[1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems &Amp; Computers

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Trellis coded quantization (TCQ) is incorporated into a transform coding structure for encoding monochrome and color images at 1 bit/pixel. Both fixed-rate and entropy-constrained designs are considered. For monochrome images, a simple 4-state fixed-rate TCQ-based system provides gains in peaksignal-to-noise ratio of up to 2.4 dB over scalar quantizer based systems, whale the entropy-constrained design provides gains of up to 7.0 dB. Gains in PSNR for color images of up to 1.6 dB and 3.9 dB are achieved for the 4-state fixed-rate and entropyconstrained TCQ-based systems, respectively. The subjective quality of the images produced by the TCQbased systems is significantly better than that of the scalar quantizer-based systems. The high frequency background noise, fuzziness, and block artifacts produced b y the scalar quantizer-based systems are virtually eliminated by the TCQ-based systems.

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An Architecture for the Compression of Hyperspectral Imagery

Pickering

Ryan

Hyperspectral Data Compression

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A comparison of vector quantization techniques in transform and subband coding of imagery

Cited by 27 publications

References 32 publications

Clustering Algorithm Based on Density of Data

Clustering Algorithm Based on Density of Data

Transform coding of monochrome and color images using trellis coded quantization

An Architecture for the Compression of Hyperspectral Imagery

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