Subband finite state scalar quantization

Naveen, T.; Woods, John W.

doi:10.1109/icassp.1993.319886

Cited by 20 publications

(8 citation statements)

References 10 publications

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“…At lower encoding rates, the method is competitive with the best previously reported results. : Naveen and Woods [20] : Said and Pearlman [23] : Sriram and Marcellin [26] : Taubman and Zakhor [29] Figure 10: Comparison of encoding performance for the`Lenna' image.…”

Section: Discussionmentioning

confidence: 99%

Comparison of different methods of classification in subband coding of images

Joshi

Jafarkhani

Kasner

et al. 1997

IEEE Trans. on Image Process.

145

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This paper investigates various classification techniques, applied to subband coding of images, as a way of exploiting the nonstationary nature of image subbands. The advantages of subband classification are characterized in a rate-distortion framework in terms of "classification gain" and overall "subband classification gain." Two algorithms, maximum classification gain and equal mean-normalized standard deviation classification, which allow unequal number of blocks in each class, are presented. The dependence between the classification maps from different subbands is exploited either directly while encoding the classification maps or indirectly by constraining the classification maps. The trade-off between the classification gain and the amount of side information is explored. Coding results for a subband image coder based on classification are presented. The simulation results demonstrate the value of classification in subband coding.

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Section: Discussionmentioning

confidence: 99%

Comparison of different methods of classification in subband coding of images

Joshi

Jafarkhani

Kasner

et al. 1997

IEEE Trans. on Image Process.

145

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“…The proposed coding scheme employs DPCM for encoding the base band and ADPCM for encoding the directional subbands. The general DPCM system is employed in our coding scheme, and we use the 3rd-order two dimensional where, (1) predictor as DPCM one as follows8' Q(i,j)=Ax(i,j-1)+ &(i-1,j-1)+ Cx(i-1,j) (4) The ADPCM system used in the proposed coding scheme is shown in Fig. 3.…”

Section: Dpcm and Adpcmmentioning

confidence: 99%

<title>Hybrid subband image coding scheme using DWT, DPCM, and ADPCM</title>

Kim

Joo

1998

SPIE Proceedings

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Subband image coding techniques have received considerable attention as a powerful source coding ones. These techniques provide good compression results, and also can be extended for progressive transmission and multiresolution analysis. In this paper, we propose a hybrid subband image coding scheme using DWT(cliscrete wavelet transform), DPCM(differential pulse code modulation), and ADPCM(adaptive DPCM). This scheme produces both simple, but significant, image compression and transmission coding.

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“…Then the optimum rate allocation problem can be stated as mm >: (4) i=1 j=1 subject to the constraint…”

Section: Motivationmentioning

confidence: 99%

<title>Comparison of different methods of classification in subband coding of images</title>

1995

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Spatially varying quantization schemes try to exploit the non-stationary nature of image subbands. One technique for spatially varying quantization is classification based on AC energy of blocks. Several different methods of subband classification have been proposed in the literature. One method is to optimally classify each subband and send the classification maps as side information. Although image subbands can be shown to be roughly uncorrelated, they are not independent. Naveen and Woods proposed a method in which classification is done based on the AC energy of the block corresponding to the same spatial location, but from the lower frequency band. In their method, inter-subband dependence is exploited to almost completely eliminate side information, albeit at the cost of decreasing classification gain.In this paper, we propose a new method of classification based on vector quantization of AC energy n-tuples formed by energies of blocks which correspond to the same spatial location in the original image but belong to different subbands. This method allows us to reduce the side information at the same time maximizing classification gain for each band under the vector constraint. The performance of the new method is compared with the other two methods. The comparison is made based on conditional entropies as well as actual bit rates. A variety of approaches have been proposed for classification of image subbands. Joshi et a!6 proposed 154 ISPIE Vol. 2418 O-8194-1765-3/95/$6.OO Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/25/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx 8 ACKNOWLEDGEMENTS

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Subband finite state scalar quantization

Cited by 20 publications

References 10 publications

Comparison of different methods of classification in subband coding of images

Comparison of different methods of classification in subband coding of images

<title>Hybrid subband image coding scheme using DWT, DPCM, and ADPCM</title>

<title>Comparison of different methods of classification in subband coding of images</title>

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