An entropy-coded lattice vector quantizer for transform and subband image coding

Mohdyusof, Z.; Fischer, Thomas R.

doi:10.1109/83.480764

Cited by 25 publications

(9 citation statements)

References 27 publications

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“…And a codebook with nonuniform point density is inferior to a uniform codebook, as long as entropy coding is being applied. Applications of entropy coded lattice quantization are presented in, e.g., [14,31,32].…”

Section: Quantizer Design For Nonuniform Sourcesmentioning

confidence: 99%

Optimization of lattices for quantization

Agrell

Eriksson

1998

IEEE Trans. Inform. Theory

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A training algorithm for the design of lattices for vector quantization is presented. The algorithm uses a steepest descent method to adjust a generator matrix, in the search for a lattice whose Voronoi regions have minimal normalized second moment. The numerical elements of the found generator matrices are interpreted and translated into exact values. Experiments show that the algorithm is stable, in the sense that several independent runs reach equivalent lattices. The obtained lattices reach as low second moments as the best previously reported lattices, or even lower. Specifically, we report lattices in 9 and 10 dimensions with normalized second moments of 0.0716 and 0.0708, respectively, and nonlattice tessellations in 7 and 9 dimensions with 0.0727 and 0.0711, which improves on previously known values. The new 9and 10-dimensional lattices suggest that Conway and Sloane's conjecture on the duality between the optimal lattices for packing and quantization might be false. A discussion of the application of lattices in vector quantizer design for various sources, uniform and nonuniform, is included.

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Section: Quantizer Design For Nonuniform Sourcesmentioning

confidence: 99%

Optimization of lattices for quantization

Agrell

Eriksson

1998

IEEE Trans. Inform. Theory

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“…Fig. 13 shows the performance of the proposed algorithms as well as that of several prominent image compression algorithms [19], [20], [29]- [32], [35] when encoding this image. Figs.…”

Section: Resultsmentioning

confidence: 99%

“…The above represent some of the drawbacks of the algorithm proposed by Barlaud [12]. The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [14], [35] and Jeong [17]. Moreover, no training and multi-quantizing (to determine the lattice parameters) are required, as opposed to Powell's algorithm [28].…”

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confidence: 99%

A novel fingerprint image compression technique using wavelets packets and pyramid lattice vector quantization

Kasaei

Deriche

Boashash

2002

IEEE Trans. on Image Process.

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A novel compression algorithm for fingerprint images is introduced. Using wavelet packets and lattice vector quantization , a new vector quantization scheme based on an accurate model for the distribution of the wavelet coefficients is presented. The model is based on the generalized Gaussian distribution. We also discuss a new method for determining the largest radius of the lattice used and its scaling factor , for both uniform and piecewise-uniform pyramidal lattices. The proposed algorithms aim at achieving the best rate-distortion function by adapting to the characteristics of the subimages. In the proposed optimization algorithm, no assumptions about the lattice parameters are made, and no training and multi-quantizing are required. We also show that the wedge region problem encountered with sharply distributed random sources is resolved in the proposed algorithm. The proposed algorithms adapt to variability in input images and to specified bit rates. Compared to other available image compression algorithms, the proposed algorithms result in higher quality reconstructed images for identical bit rates.

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“…A spherical LVQ using the RE 8 lattice [11] is used in the AMR-WB+ audio coding standard [12]. A pyramid-based LVQ has been used for image coding [13]- [14]. Lattice-based vector quantization offers the potential of relatively simple quantization and granular coding gain [15].…”

Section: Introductionmentioning

confidence: 99%

Block-adaptive lattice vector quantization in image coding

Patchoo

Fischer

2012

2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolog

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A subband image coding algorithm is described based on lattice-based spherical VQ and lattice-based pyramid VQ. The algorithm partitions a subband (or wavelet) decomposed image into blocks of various sizes, depending on their energy and complexity constraints on the enumeration encoding of lattice codevectors. Each block is lattice vector quantized and encoded using a product code. The algorithm is simple and effective, exploiting energy clustering in a wavelet transformed image. Using the cubic lattice, the algorithm provides performance competitive with the set partitioning in hierarchical trees (SPIHT) algorithm.Index Terms-Lattice vector quantization, image coding, and wavelet transform.

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An entropy-coded lattice vector quantizer for transform and subband image coding

Cited by 25 publications

References 27 publications

Optimization of lattices for quantization

Optimization of lattices for quantization

A novel fingerprint image compression technique using wavelets packets and pyramid lattice vector quantization

Block-adaptive lattice vector quantization in image coding

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