Subband image coding using a fixed-rate lattice vector quantizer

Mohdyusof, Z.; Fischer, Thomas R.

doi:10.1109/icip.1995.529049

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Since the L1-RQ and 0-RQ schemes differ only in the coding of the LFS, this occurs when the distribution of the LFS tends toward uniform in which case the COSQ performance is slightly better for quantization of a uniform than for a Gaussian distributed source [2]. Table IV summarizes the results of the quantization schemes reported in [26] (SVQ-DCT), [27] (PVQ-SUB), [29] (LVQ-SUB), and [15] [S/C-SUB(D)] for encoding the Lenna image. The performance of the proposed scheme A-RQ is also included for comparison.…”

Section: A Subband Image Codingmentioning

confidence: 94%

“…The PSNR value of this scheme is obtained from the performance curve printed in the paper. In [29], the performance results of subband coding using fixed-rate lattice VQ are reported. The image is decomposed into 13 subbands.…”

Section: A Subband Image Codingmentioning

confidence: 99%

“…7 shows the little girl image coded using the A-RQ and LVQ-SUB [29] systems, for an encoding rate of 0.5 b/pixel and channel bit error rate . The A-RQ reconstructed image shown is the worst of 20 different channel error simulations.…”

Section: Reconstructed Imagesmentioning

confidence: 99%

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Image coding using robust quantization for noisy digital transmission

Chen¹,

Fischer

1998

IEEE Trans. on Image Process.

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A robust quantizer is developed for encoding memoryless sources and transmission over the binary symmetric channel (BSC). The system combines channel optimized scalar quantization (COSQ) with all-pass filtering, the latter performed using a binary phase-scrambling/descrambling method. Applied to a broad class of sources, the robust quantizer achieves the same performance as the Gaussian COSQ for the memoryless Gaussian source. This quantizer is used in image coding for transmission over a BSC. The peak signal-to-noise ratio (PSNR) performance degrades gracefully as the channel bit error rate increases.

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Section: A Subband Image Codingmentioning

confidence: 94%

Section: A Subband Image Codingmentioning

confidence: 99%

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Image coding using robust quantization for noisy digital transmission

Chen¹,

Fischer

1998

IEEE Trans. on Image Process.

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“…In the simplest, termed A-RQ, each subband is separately all-pass filtered (using the phase scrambling method) and encoded using the robust quantizer. Table 2 compares the peak signal-to-noise ratio (PSNR) performance of the proposed method (A-RQ) to that of several recently proposed vector quantization methods ( [15] (SVQ-DCT), [16] (PVQ-SUB), [17] (LVQ-SUB)), and [18] (S/C-SUB(D)) for encoding the Lenna image. In [15], an adaptive 2-D DCT image coding system is used.…”

Section: I11 Image Codingmentioning

confidence: 99%

“…In [17], the performance results of subband coding using fixed-rate lattice VQ are reported. The image is decomposed into 13 subbands.…”

Section: I11 Image Codingmentioning

confidence: 99%

Robust quantization for image coding and noisy digital transmission

Chen

Fischer

Proceedings of Data Compression Conference - DCC '96

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A robust quantizer is developed for encoding a variety of memorylesssources and transmission over the binary symmetric channel (BSC). The system combines channel optimized scalar quantization (COSQ) with all-pass filtering, the latter performed using a binary phase-scrambling/descrambling method. Applied to a broad class ofsources, the robust quantizer achieves the same performance as the Gaussian COSQ for the memoryless Gaussian source. This quantizer is used for image coding for transmission over a BSC. An explicit error protection code is used only to protect side information. The PSNR performance degrades gracefully as the channel bit error rate increases. I IntroductionApproaches to designing scalar and vector quantizers for use over a binary symmetric channel (BSC) include Farvardin and Vaishampayan's channel optimized scalar quantization (COSQ) [l], and channel optimized vector quantization (COVQ) [2], Ayanogu and Gray's trellis waveform coding [3], and Wang and Fischer's channel optimized trellis coded quantization (COTCQ) [4]. For a given VQ design, Farvardin [5], Zeger and Gersho [6], and Hagen and Hedelin [7] have developed algorithms to assign binary codewords to index the VQ codevectors to minimize the distortion caused by channel errors. This has been called pseudo-Gray VQ [6]. Laroia and Farvardin's scalar vector quantizer (SVQ) [8] is representative of the good VQ performance that can be achieved, provided channel errors are not a significant problem. Figure 1 compares the performance of these methods for a memoryless Gaussian source. It is evident that if the channel bit error rate is significant (larger than about lo-') very

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