Fast fuzzy vector quantization

Tsekouras, George E.; Darzentas, Dimitrios; Drakoulaki, Ioanna; Niros, Antonios D.

doi:10.1109/fuzzy.2010.5584446

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…4. At current iteration G, the individual candidates of the population are updated using the mutation and crossover strategies using equations (6)- (7). Boundary control is applied so that the solutions remain within the limits of the search space.…”

Section: Fitness Functionmentioning

confidence: 99%

“…Such significance of Codebook training gave new impetus to many researchers leading to the proliferation of researches to design codebook using several projected approaches. Vector Quantization methods are categorized into two classes: crisp and fuzzy [7]. Sensitive to codebook initialization, Crisp VQ follows a hard decision making process.…”

Section: Introductionmentioning

confidence: 99%

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Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression

Nag¹

2017

Preprint

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Vector Quantization (VQ) is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in Vector Quantization. Linde-Buzo-Gray (LBG) is a traditional method of generation of VQ Codebook which results in lower PSNR value. A Codebook affects the quality of image compression, so the choice of an appropriate codebook is a must.Several optimization techniques have been proposed for global codebook generation to enhance the quality of image compression. In this paper, a novel algorithm called IDE-LBG is proposed which uses Improved Differential Evolution Algorithm coupled with LBG for generating optimum VQ Codebooks. The proposed IDE works better than the traditional DE with modifications in the scaling factor and the boundary control mechanism. The IDE generates better solutions by efficient exploration and exploitation of the search space. Then the best optimal solution obtained by the IDE is provided as the initial Codebook for the LBG. This approach produces an efficient Codebook with less computational time and the consequences include excellent PSNR values and superior quality reconstructed images. It is observed that the proposed IDE-LBG find better VQ Codebooks as compared to IPSO-LBG, BA-LBG and FA-LBG.

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Section: Fitness Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression

Nag¹

2017

Preprint

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“…The size and definition of the population of the codebook or training (updated during the measurement) has two critical parameters that determine the efficiency of a VQ [1][2][3][4][5]. There are several models that reduce both storage and computational load, but the problem is that those do not always match with the vector patterns of the incoming signal due to a phase shift [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Voice Coding with Significant Impulses Modeling and Redundant Waveform Recycling

Arriaga¹,

Infante²,

García³

2013

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This paper proposes a voice codification based on two algorithms that make the wave form codification in time domain. The first uses the significant impulse model (SIM), which has as a goal to operate as an endpoint detector and as a dawn sampling, through the detection and selection of the significant valleys and crests; the second algorithm, is a redundant wave-form recycler (RWR) that uses an architecture based on fuzzy logic with an accumulative memory. The fuzzy algorithm obtains the similitude grade between the redundant wave forms, this with the objective of save into an knowledge base the patterns, based on the no supervised learning and when there are into memory, automatically there will be used to identified their arrive respect to the input signal, substituting the input block by the correspondent pattern into memory. This decoding process is using the SIM interpolation with a memory in accordance to the RWR.

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“…A significant challenge in VQ is the generation of an optimal codebook, which reduces the error between the compressed and the decompressed images. VQ approaches are of two types, such as crisp and fuzzy approaches (Tsekouras, Darzentas, Drakoulaki, & Niros, 2010). The crisp VQ depends on hard decision-making procedure and is sensitive to initialize the codebook.…”

mentioning

confidence: 99%

An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Geetha¹,

Anitha

Elhoseny

et al. 2020

Expert Systems

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Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde-Buzo-Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2-LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search-based LBG (CS-LBG), Firefly-based LBG (FF-LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2-LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS-LBG, FA-LBG, and JPEG2000 methods. The experimental values revealed that the L2-LBG process yielded effective compression performance with a better-quality reconstructed image.

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Fast fuzzy vector quantization

Cited by 11 publications

References 23 publications

Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression

Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression

Fuzzy Voice Coding with Significant Impulses Modeling and Redundant Waveform Recycling

An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

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