Optimization Vector Quantization by Adaptive Associative-Memory-Based Codebook Learning in Combination with Huffman Coding

In the recent days, the importance of image compression techniques is exponentially increased due to the generation of massive amount of data which needs to be stored or transmitted. Numerous approaches have been presented for effective image compression by the principle of representing images in its compact form through the avoidance of unnecessary pixels. Vector quantization (VA) is an effective method in image compression and the construction of quantization table is an important process is an important task. The compression performance and the quality of reconstructed data are based on the quantization table, which is actually a matrix of 64 integers. The quantization table selection is a complex combinatorial problem which can be resolved by the evolutionary algorithms (EA). Presently, EA became famous to resolve the real world problems in a reasonable amount of time. This chapter introduces Firefly (FF) with Teaching and learning based optimization (TLBO) algorithm termed as FF-TLBO algorithm for the selection of quantization table and introduces Firefly with Tumbling algorithm termed as FF-Tumbling algorithm for the selection of search space. As the FF algorithm faces a problem when brighter FFs are insignificant, the TLBO algorithm is integrated to it to resolve the problem and Tumbling efficiently train the algorithm to explore all direction in the solution space. This algorithm determines the best fit value for every bock as local best and best fitness value for the entire image is considered as global best. When these values are found by FF algorithm, compression process takes place by efficient image compression algorithm like Run Length Encoding and Huffman coding. The proposed FF-TLBO and FF-Tumbling algorithm is evaluated by comparing its results with existing FF algorithm using a same set of benchmark images in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR). The obtained results ensure the superior performance of FF-TLBO and FF-Tumbling algorithm over FF algorithm and make it highly useful for real time applications.

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VLSI Implementation of a Neural Associative Memory and its Application to Vector Quantization

International Neural Network Conference ◽

10.1007/978-94-009-0643-3_29 ◽

1990 ◽

pp. 635-638

Author(s):

Rodney M. Goodman ◽

Tzi-Dar Chiueh

Keyword(s):

Associative Memory ◽

Vector Quantization ◽

Vlsi Implementation

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Improved vector quantization scheme for grayscale image compression

Opto-Electronics Review ◽

10.2478/s11772-012-0016-z ◽

2012 ◽

Vol 20 (2) ◽

Cited By ~ 5

Author(s):

Y. Hu ◽

W. Chen ◽

C. Lo ◽

J. Chuang

Keyword(s):

Image Quality ◽

Vector Quantization ◽

Image Coding ◽

Linear Prediction ◽

Mean Value ◽

Huffman Coding ◽

Quantization Scheme ◽

Image Block ◽

Lossless Coding

AbstractThis paper proposes an improved image coding scheme based on vector quantization. It is well known that the image quality of a VQ-compressed image is poor when a small-sized codebook is used. In order to solve this problem, the mean value of the image block is taken as an alternative block encoding rule to improve the image quality in the proposed scheme. To cut down the storage cost of compressed codes, a two-stage lossless coding approach including the linear prediction technique and the Huffman coding technique is employed in the proposed scheme. The results show that the proposed scheme achieves better image qualities than vector quantization while keeping low bit rates.

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