Image coding using pyramid vector quantization of subband coefficients

Tsern, E.K.; Meng, Tao

doi:10.1109/icassp.1994.389379

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…Huffman coding of coefficients is acceptable when packet boundaries fall between codewords, while segmenting an arithmetically coded stream across several packets is not acceptable, as loss of any packet destroys synchronization at the decoder. Proposed subband coding techniques that can immediately be used with the described intraband coding, interleaving, and packetization requirements include [12][13][14][15][16][17].…”

Section: B Implementation Aspectsmentioning

confidence: 99%

“…Note that if the codeword lengths are fixed, and the increase can be 0 if P is a multiple of all 's. For example, for the fixed-length technique presented in [12] (fixed-rate lattice vector quantization, for lena at 0.25 bits/pixel, PSNR = 31.4 dB), with ATM packets with , the increase is 14%. For the variable-length technique presented in [17] (entropy-constrained lattice vector quantization, for lena at 0.136 bits/pixel, PSNR = 30.9 dB), again using ATM packets the increase is 4%.…”

Section: B Implementation Aspectsmentioning

confidence: 99%

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Subband-coded image reconstruction for lossy packet networks

Hemami

Gray²

1997

IEEE Trans. on Image Process.

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Transmission of digital subband-coded images over lossy packet networks presents a reconstruction problem at the decoder. This paper presents two techniques for reconstruction of lost subband coefficients, one for low-frequency coefficients and one for high-frequency coefficients. The low-frequency reconstruction algorithm is based on inherent properties of the hierarchical subband decomposition. To maintain smoothness and exploit the high intraband correlation, a cubic interpolative surface is fit to known coefficients to interpolate lost coefficients. Accurate edge placement, crucial for visual quality, is achieved by adapting the interpolation grid in both the horizontal and vertical directions as determined by the edges present. An edge model is used to characterize the adaptation, and a quantitative analysis of this model demonstrates that edges can be identified by simply examining the high-frequency bands, without requiring any additional processing of the low-frequency band. High-frequency reconstruction is performed using linear interpolation, which provides good visual performance as well as maintains properties required for edge placement in the low-frequency reconstruction algorithm. The complete algorithm performs well on loss of single coefficients, vectors, and small blocks, and is therefore applicable to a variety of source coding techniques.

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Section: B Implementation Aspectsmentioning

confidence: 99%

Section: B Implementation Aspectsmentioning

confidence: 99%

Subband-coded image reconstruction for lossy packet networks

Hemami

Gray²

1997

IEEE Trans. on Image Process.

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“…Furthermore since subband and DCT coefficients can be modeled as independent Laplacian random variables, PVQ is very effective for quantization of subband or transform-coded image frames [l]. Therefore, due t o its high compression ratio, reasonable computation complexity, error-resiliency, and simple buffering scheme, PVQ is an ideal 0-7803-3134-6196 $5.00 0 1996 IEEE candidate for compressing transform-coded image data, especially in noisy wireless channels [7]. For moderate size dimension, a product PVQ [2] can be used to reduce the distortion introduced by an insufficient vector dimension.…”

Section: Introductionmentioning

confidence: 99%

“…For moderate size dimension, a product PVQ [2] can be used to reduce the distortion introduced by an insufficient vector dimension. Both PVQ and product PVQ have been successfully applied to quantize DCT coefficients [6] and subband coding coefficients [I] [7].…”

Section: Introductionmentioning

confidence: 99%

VLSI array architectures for pyramid vector quantization

Jung

Burleson

VLSI Signal Processing, IX

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We present parallel algorithms and array architectures for pyramid vector quantization (PVQ) [2] for use in image coding in lowpower wireless systems. 130th encoding and decoding algorithms have data-dependent iteration b l x m d s and data-dependent dependencies which prevent efficient parallelization of the algorithms. We perform an algorithmic transformation 1 3 1 to convert data-dependent regular algorithms to equivalent data-independent regular algorithms. The resulting regular algorithms exhibit modular and regular structures; hence, they are well suited for VLSI array implementation. Based on our parallel algorithm and systematic design methodologies [4], linear array architectures have been developed. Both encoder and decoder architectures consist of L identical processors with local interconnections and provide O( L) speedu p over a sequential implementation, where L is the dimension of a vector. The architectures aclhieve 100 % processor utilization and permit power savings through early completion detection.

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Jung

Burleson

1998

The Journal of VLSI Signal Processing

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Image coding using pyramid vector quantization of subband coefficients

Cited by 10 publications

References 9 publications

Subband-coded image reconstruction for lossy packet networks

Subband-coded image reconstruction for lossy packet networks

VLSI array architectures for pyramid vector quantization

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