An optimization of finite-state vector quantization for image compression

Cziho, A.; Solaiman, Basel; Lovanyi, I.; Cazuguel, Guy; Roux, Christian

doi:10.1016/s0923-5965(99)00012-0

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…FSVQ is a finite-state machine, in which each state corresponds to a different state codebook. A conventional FSVQ [15,20] usually consists of a large codebook, from which a fixed number of codevectors are chosen to generate a state codebook of much smaller size for each input vector. This state codebook is composed of the best matching codevectors that could be found in the large codebook for encoding the current input vector.…”

Section: Main Finite-state Vector Quantisermentioning

confidence: 99%

“…To better recover the split loss of a SVQ, the finite-state VQ (FSVQ) can usually be resorted to, for which is able to efficiently take advantage of the inter-frame dependencies. FSVQ [15], which incorporates memory into a memoryless VQ, is essentially a prediction-based technique. An FSVQ can be regarded as a finite-state machine [16], which contains multiple states, each corresponding to a certain state codebook.…”

Section: Introductionmentioning

confidence: 99%

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Finite‐state entropy‐constrained vector quantiser for audio modified discrete cosine transform coefficients uniform quantisation

Jiang

Yin

Liu

2015

IET signal process.

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In this paper, an entropy-constrained vector quantiser (ECVQ) scheme with finite memory, called finite-state ECVQ (FS-ECVQ), is presented. This scheme consists of a finite-state vector quantiser (FSVQ) and multiple component ECVQs. By utilising the FSVQ, the inter-frame dependencies within source sequence can be effectively exploited and no side information needs to be transmitted. By employing the ECVQs, the total memory requirements of FS-ECVQ can be efficiently decreased while the coding performance is improved. An FS-ECVQ, designed for the modified discrete cosine transform coefficients coding, was implemented and evaluated based on the unified speech and audio coding (USAC) scheme. Results showed that the FS-ECVQ achieved reduction of the total memory requirements by 92.3%, compared with the encoder in USAC working draft 6 (WD6), and over 10%, compared with the encoder in USAC final version (FINAL), while maintaining coding performance similar to FINAL, which was about 4% better than that of WD6.

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Section: Main Finite-state Vector Quantisermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finite‐state entropy‐constrained vector quantiser for audio modified discrete cosine transform coefficients uniform quantisation

Jiang

Yin

Liu

2015

IET signal process.

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“…In [14], a method for designing predictive vector quantizer using deterministic annealing is proposed. An approach to design an optimal FSVQ is proposed in [7]. Interestingly, in this paper the codebook is built using the SOM algorithm.…”

Section: The Encodermentioning

confidence: 99%

“…Thus often a threshold of matching is used. If no codevector with match exceeding the threshold is found in the state codebook, the system is said to 'derail' [7]. In such a situation usually an exhaustive search over the super codebook is performed for re-initializing the search process.…”

Section: Strategy 1: Restricted Window Search Over Som Latticementioning

confidence: 99%

Fast codebook searching in a SOM-based vector quantizer for image compression

Laha

Chanda

Pal

2007

SIViP

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We propose a novel method for fast codebook searching in self-organizing map (SOM)-generated codebooks. This method performs a non-exhaustive search of the codebook to find a good match for an input vector. While performing an exhaustive search in a large codebook with high dimensional vectors, the encoder faces a significant computational barrier. Due to its topology preservation property, SOM holds a good promise of being utilized for fast codebook searching. This aspect of SOM remained largely unexploited till date. In this paper we first develop two separate strategies for fast codebook searching by exploiting the properties of SOM and then combine these strategies to develop the proposed method for improved overall performance. Though the method is general enough to be applied for any kind of signal domain, in the present paper we demonstrate its efficacy with spatial vector quantization of gray-scale images.

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