Subband coding of color images using finite state vector quantization

Kim, C.-S.; Bruder, J.A.; Smith, M.J.T.; Mersereau, R.M.

doi:10.1109/icassp.1988.196694

Cited by 34 publications

(5 citation statements)

References 3 publications

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“…In 1988, Kim et al 13 described SB coding of color images using Finite State Vector Quantization (FSVQ). A memoryless VQ exploited the statistical redundancy between pixels within a vector to reduce the bit rate.…”

Section: Transform Codingmentioning

confidence: 99%

Image Compression System for Mobile Communication: Advancement in the Recent Years

Reaz

Akter

Mohd-Yasin

2006

J CIRCUIT SYST COMP

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Mobile communication has a great potential to the users due to fulfilling the dreams of real-time multimedia communication like voice, image, and text. The huge amount of data redundancy in still image should be compressed using exact image compression algorithm (ICA) before transmitting via wireless channel. Thus, an ICA should be adaptive, simple, and cost-effective and suitable for feasible implementation. Hardware implementation of the different algorithms has improved using modern, fast, and cost-effective technologies. The main aim of this paper is to review and demonstrate various ICAs developed based on image transmission via wireless channel as well as their hardware implementation. Finally, this review makes bridge for researchers to the future relative studies between different algorithms and architectures, and stands as a reference point for developing more controlling and flexible structures.

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Section: Transform Codingmentioning

confidence: 99%

Image Compression System for Mobile Communication: Advancement in the Recent Years

Reaz

Akter

Mohd-Yasin

2006

J CIRCUIT SYST COMP

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“…The type is determined by projecting the 3 3 LL region onto the orthogonal feature vectors determined by F rei and Chen 8], which m a t c h \edge", \line", and \ripple" characteristics. More specifically, if the centre coe cient of a template is written as t(0 0), then the coe cients can be formed into the following vector, t = t(;1 ;1) t (;1 0) t (0 0) t (1 1)] T (4) and each projection calculated as = ( b ij ; m) T t (5) where m is the vector with each component equal to the mean pixel value in the 3 3 region. The result for each feature is then labeled as one of f edge1 edge2 line1 line2 rip1 rip2 g depending on the template.…”

Section: Predicting the Highpass Subbandsmentioning

confidence: 99%

Subband prediction using leakage information in image coding

Vaisey

1995

IEEE Trans. Commun.

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This paper examines the potential uses of interband leakage in image coders based on subband decomposition. The approach taken is to use the lowpass subband to predict the signal behavior in the other bands. This information is then used to both inform the coder/decoder as to likely locations for signi cant energy in the highpass bands, and to form a di erence signal that can be coded at a lower rate. These methods give signi cant gains for some image/ lter combinations, but in general it seems that analysis lters producing low correlation should be used if possible.

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“…In quantizing the SWT coefficients, various approaches have been proposed in the literature that explore their statistical characteristics and perceptual relevance, as well as, the existing inter/intra band correlation [ 5,6,7,19,20]. For scalar quantization, it was shown that uniform quantization allows the optimum entropy coding of coefficients {21].…”

Section: Perception Based Quantizationmentioning

confidence: 99%

<title>Image compression using subband/wavelet transform and adaptive multiple-distribution entropy coding</title>

1992

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Image compression methods using subband/wavelet transform and adaptive multiple distribution entropy coding (AMDEC) are presented. The methods are suitable for the coding of still images, as well as, motion compensated prediction error images for video compression. First, the subband/wavelet transform coefficients are uniformly quantized. For still image coding perception based weighted quantization is used. Space filling scanning, subband partitioning and classification methods are used in dividing the original source of quantized coefficients into a number of subsources with corresponding distributions. A hierarchical partition priority coding (PPC) approach is followed, that is, given a suitable partitioning of their range, the transform coefficients are ordered based on their magnitude. AMDEC is applied to the output of PPC, which contains magnitude and location information, using adaptive arithmetic coding based on the histograms of the various subsources. Experimental results on standard monochromatic images and video-conference image sequences demonstrate the great performance of the proposed AMDEC methods.

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Subband coding of color images using finite state vector quantization

Cited by 34 publications

References 3 publications

Image Compression System for Mobile Communication: Advancement in the Recent Years

Image Compression System for Mobile Communication: Advancement in the Recent Years

Subband prediction using leakage information in image coding

<title>Image compression using subband/wavelet transform and adaptive multiple-distribution entropy coding</title>

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