Image coding using entropy-constrained residual vector quantization
F Kossentini, MJT Smith… - IEEE transactions on …, 1995 - ieeexplore.ieee.org
F Kossentini, MJT Smith, CF Barnes
IEEE transactions on image processing, 1995•ieeexplore.ieee.orgAn entropy-constrained residual vector quantization design algorithm is used to design
codebooks for image coding. Entropy-constrained residual vector quantization has several
important advantages. It can outperform entropy-constrained vector quantization in terms of
rate-distortion performance, memory, and computation requirements. It can also be used to
design vector quantizers with relatively large vector sizes and high output rates.
Experimental results indicate that good image reproduction quality can be achieved at …
codebooks for image coding. Entropy-constrained residual vector quantization has several
important advantages. It can outperform entropy-constrained vector quantization in terms of
rate-distortion performance, memory, and computation requirements. It can also be used to
design vector quantizers with relatively large vector sizes and high output rates.
Experimental results indicate that good image reproduction quality can be achieved at …
An entropy-constrained residual vector quantization design algorithm is used to design codebooks for image coding. Entropy-constrained residual vector quantization has several important advantages. It can outperform entropy-constrained vector quantization in terms of rate-distortion performance, memory, and computation requirements. It can also be used to design vector quantizers with relatively large vector sizes and high output rates. Experimental results indicate that good image reproduction quality can be achieved at relatively low bit rates. For example, a peak signal-to-noise ratio of 30.09 dB is obtained for the 512/spl times/512 LENA image at a bit rate of 0.145 b/p.< >
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