NDLI: A fuzzy neural network based adaptive predictor with P-controller compensation for lossless compression of images

Content Provider	IEEE Xplore Digital Library
Author	Ching-Hung Lee Lih-Jen Kau Yuan-Pei Lin
Copyright Year	2009
Description	Author affiliation: Dept. Electrical Engr., Yuan Ze Univ., Chung-li, Taoyuan, Taiwan, R.O.C. (Ching-Hung Lee) \|\| Dept. Electrical and Control Engr., National Chiao Tung Univ., Hsinchu, Taiwan, R.O.C. (Yuan-Pei Lin) \|\| Dept. Computer and Communication Engr., Dahan Inst. Technology, Hualien, Taiwan, R.O.C. (Lih-Jen Kau)
Abstract	Predictively encoded techniques are commonly used for lossless compression of images for its effectiveness of removing statistical redundancy between pixels. However, there can be large prediction errors for pixels around boundaries. In this paper, we introduce techniques commonly used in control systems to enhance the coding efficiency of predictive coding. Actually, the predictive coding system behaves just like a multi-input single-output system with the predictor itself can be taken as the system model. When compared with the purpose of a control system, which is to follow the system command as precisely as possible, we find the objective of both systems are the same. Moreover, an edge or a boundary among image pixels can be regarded as a step command in control systems. These observations lead to the idea of using control technologies to improve prediction result for pixels around boundaries. To realize this idea, we use an adaptive Takagi-Sugeno fuzzy neural network (TS-FNN) as the predictor. Furthermore, the widely used proportional controller in control system is implemented implicitly in the consequent part of the network so that the prediction error can be further compensated for pixels around boundaries. We find in experiments that the proposed approach can have a very good prediction result even without using any online training area for network adaptation process. This makes the proposed system more feasible under limited resources. Finally, comparisons to existing state-of-the-art lossless predictors and coders will be given to highlight the advantages of the proposed novel approach.
Starting Page	633
Ending Page	636
File Size	630947
Page Count	4
File Format	PDF
ISBN	9781424438273
DOI	10.1109/ISCAS.2009.5117828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-24
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Adaptive systems Image coding Control systems Fuzzy control Pixel Predictive coding Proportional control Redundancy Predictive models
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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