Computation of the complexity of vector quantizers by affine modeling

Seraco, Estevan P.; Gomes, Jonas

doi:10.1016/j.sigpro.2010.10.015

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…In order to perform this optimization, an objective function J triple = D + λH + γθ needs to be minimized. The value of λ can be computed using a procedure similar to the one mentioned in Section IV and, then, the triple optimization problem can be treated as a new optimization problem with two objectives (a similar solution in the particular SQ and ECVQ cases was advanced in [14]). To perform entropy-constrained optimization, a certain value of λ is associated with a specific (D,H) pair, and then, given this λ, the cost J of this pair is reduced.…”

Section: Rate-distortion-complexity Optimizationmentioning

confidence: 99%

Neural network design for data compression based on Kernel PCA: Rate-distortion and complexity analysis

Elias

Gomes

2015

2015 Latin America Congress on Computational Intelligence (LA-CCI)

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This work presents a study of the properties of a non-linear vector quantization (VQ) method based on Kernel Principal Component Analysis (KPCA), focused on the complexity and viability of implementing this method in image processing. The theory supporting this method is described and then the method is compared to traditional quantization methods, as scalar quantization and entropy-constrained vector quantization. The main characteristics compared are the entropy versus distortion curves, illustrating the quantizers rate-distortion performance, and the complexity associated with the quantization process, as a function of the computational cost required in digital implementation. Finally, this work introduces a complexity-constrained approach to quantizer design.

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Section: Rate-distortion-complexity Optimizationmentioning

confidence: 99%

Neural network design for data compression based on Kernel PCA: Rate-distortion and complexity analysis

Elias

Gomes

2015

2015 Latin America Congress on Computational Intelligence (LA-CCI)

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“…By iterating steps (2) and (3), the algorithm converges to ideal codebook and partition . Due to complexity constraints [13], the ideal partition condition cannot be implemented at the focal plane. Instead, an approximated version was implemented as described in [9].…”

Section: A Vector Quantization Fundamentalsmentioning

confidence: 99%

Codebook improvements for a CMOS imager with focal-plane vector quantization

Filho¹,

Gomes²,

Petraglia³

2013

2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS)

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In previous works a 32 x 32 pixel CMOS image sensor featuring focal-plane image compression based on differential pulse-code modulation and vector quantization was presented. Images compressed with this imager were reconstructed at peak signal-to-noise ratios around 18 dB, for bit rates below 0.94 bit per pixel. In this work, we focus on the improvement of the vector quantization stage of the previously proposed imager. We use experimentally captured photographs to update the codebook that is used at the decoder side. With the new codebooks, we are able to reconstruct texture feature vectors with a mean squared error (MSE) that is consistently smaller than the MSE obtained with the originally designed codebook.I.

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Efficient VQ-based image coding scheme using inverse function and lossless index coding

Chen

et al. 2013

Signal Processing

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Computation of the complexity of vector quantizers by affine modeling

Cited by 6 publications

References 38 publications

Neural network design for data compression based on Kernel PCA: Rate-distortion and complexity analysis

Neural network design for data compression based on Kernel PCA: Rate-distortion and complexity analysis

Codebook improvements for a CMOS imager with focal-plane vector quantization

Efficient VQ-based image coding scheme using inverse function and lossless index coding

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