Vector quantisation based on a quasi-binary search algorithm

Yan, Long; Hwang, Shaw‐Hwa

doi:10.1049/iet-ipr.2010.0120

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…With the performance of a full search algorithm as a benchmark, Table 3 gives a comparison on the search load and QA among multiple TIE (MTIE) [15], TIE with dynamic and intersection rules (DI-TIE) [16], TSVQ and QBSVQ [19]. The search load is compared in terms of the average number of codeword searches, and is expressed in terms of CS as well.…”

Section: Resultsmentioning

confidence: 99%

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Efficient binary search space‐structured VQ encoder applied to a line spectral frequency quantisation in G.729 standard

et al. 2016

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Section: Resultsmentioning

confidence: 99%

“…In addition, a quasi-binary search (QBS) algorithm [19] is presented and a trade-off analysis between TSVQ and TIE is made to reduce the computational complexity of a VQ encoding. The performance of a QBS algorithm is superior to TIE-based approaches, particularly when performed on uncorrelated input signals.…”

Section: Introductionmentioning

confidence: 99%

Efficient binary search space‐structured VQ encoder applied to a line spectral frequency quantisation in G.729 standard

et al. 2016

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“…[20] have presented a vector quantization (VQ) based compression technique to enhance the standard of vector quantization compressed image and produce lossless compression. Some of the vector quantization techniques are tree‐structured vector quantization [21], side match vector quantization (SMVQ) [22], and finite state VQ [23]. After applying quantization, the high‐frequency coefficients or alternating current (AC) near to the first element in DCT, called direct current term are almost zero, when divided by the larger quantum values.…”

Section: Introductionmentioning

confidence: 99%

Fast and efficient lossless encoder in image compression with low computation and low memory

Karthikeyan

Saravanakumar²,

Sivakumar

2021

IET Image Processing

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This research focuses on frequency-based lossless new encoding technique with low computation and low memory scheme. This encoding technique doesn't require, any table similar to Huffman and Golomb Rice encoder, and doesn't take high computation time like an arithmetic encoder. For better comparative results, nearly 200 standard images were tested with the proposed encoder and their results were compared with the standard encoders. Based on the analysis of results reported in the experimental part, the proposed scheme in Discrete Cosine Transformation (DCT) achieves a high Compression Ratio (CR) of 97.68 %, Bits Per Pixel (BPP) of 0.19, Peak Signal To Noise Ratio (PSNR) of 27.62 dB for pepper image, CR of 98.65%, BPP of 0.11 and PSNR of 29.24 dB for house image, CR of 98.58 %, BPP of 0.11, PSNR of 30.59 dB for pepper image, CR of 98.37%, BPP of 0.13, PSNR of 31.37dB for house image in Discrete Wavelet Transformation (DWT) than compared to other encoders. It consumes less computation time to attain superior CR with a better quality of reconstructed picture than DCT based Huffman, Golomb-Rice (GR) coder and Low Complexity, Low memory Entropy Coder (LLEC), DWT based Arithmetic, GR code, LLEC.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…However, it cannot be ensured that the selected codeword is exactly the one closest to the input vector, leading to a decline in compression performance. Thus, strategies are proposed in [7,8] to deal with the issue of search accuracy improvement of TSVQ, whereas the issue of computational load reduction is hardly addressed therein. A combined version of a dynamic TIE (DTIE) and TSVQ, referred to as DTIE-TSVQ, is presented in this letter to achieve the aim of 100% search accuracy while preserving the excellent computational load reduction feature in TSVQ.…”

Section: Introductionmentioning

confidence: 99%

An efficient tree‐structured vector quantization using dynamic triangular inequality elimination

Chu

Yeh

Hwang

2014

IEEJ Transactions Elec Engng

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As applied to a vector quantization (VQ) codebook search, a combined version of a dynamic triangular inequality elimination (DTIE) and a tree-structured VQ (TSVQ) algorithm, designated as the DTIE-TSVQ approach, is presented in this letter as an efficient way to reach the aim of search performance improvement by successive updating of the search scope and reduced search load through the DTIE algorithm. In this manner, this proposal features the combined advantages of a TIE and a TSVQ algorithm such that 100% search accuracy is rendered together with a remarkable reduction in computational load. At the end of this work, this proposal is validated as a superior algorithm over TIE and conventional TSVQ algorithms by a high computational load saving up to 97.45% when dealing with line spectral frequency (LSF) quantization in a G.729 speech codec and image VQ encodings.

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Vector quantisation based on a quasi-binary search algorithm

Cited by 9 publications

References 15 publications

Efficient binary search space‐structured VQ encoder applied to a line spectral frequency quantisation in G.729 standard

Efficient binary search space‐structured VQ encoder applied to a line spectral frequency quantisation in G.729 standard

Fast and efficient lossless encoder in image compression with low computation and low memory

An efficient tree‐structured vector quantization using dynamic triangular inequality elimination

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