Neural network-based codebook search for image compression

Bodruzzaman, M. and Gupta, R. and Karim, M.R. and Bodruzzaman, S. (2000) Neural network-based codebook search for image compression. In: IEEE SoutheastCon 2000 'Preparing for the New Millennium', 2000, Nashville, TN, USA.

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Abstract

This paper presents an efficient and fast encoding of still images using feedforward neural network technique for codebook search. The image to be coded is first clustered into a small subset of neighboring images and then the neural network-based encoder is used to find the best matching code sequences in the codebook. This subset is then used as a candidate set and an exhaustive search is then performed within this subset to find an optimal code sequence which minimizes the perceptual error between coded and decoded images. In this work, a generic codebook is developed using non-causal Differential Pulse Coded Modulation (DPCM) with residual mean removal and vector quantization using Linde, Buzo and Gray (LBG) method. The codebook is analyzed to identify a pattern in the codebook. This pattern is used to train a neural network to obtain the approximate index of the pattern in the codebook. Then, an extensive search is done around this approximate position identified by the neural network to obtain the nearest neighbor of the pattern. Since the candidate set is usually much smaller that the whole code book, there is a substantial saving in codebook search time for coding an image as compared to the traditional method using full codebook search by LBG algorithm.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Conference code: 57263 Export Date: 16 December 2013 Source: Scopus CODEN: CPISD Language of Original Document: English Correspondence Address: Bodruzzaman, M.; Tennessee State Univ, Nashville, United States Sponsors: IEEE Region-3; Vanderbilt University; Tennessee State University; Tennesssee Technological University
Uncontrolled Keywords: Algorithms, Coding errors, Computational complexity, Decoding, Feedforward neural networks, Fractals, Pattern matching, Pulse code modulation, Vector quantization, Wavelet transforms, Codebook search, Differential pulse coded modulation, Fractal transforms, Generic codebook, Linde-Buzo and Gray method, Matching code sequence, Image coding
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 02 Jan 2014 06:59
Last Modified: 02 Jan 2014 06:59
URI: http://eprints.um.edu.my/id/eprint/8761

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