Browsing by Author "Kau, Lih-Jen"

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A Fuzzy Neural Network Based Adaptive Predictor with P-Controller Compensation for Lossless Compression of Images
(2009)
Lee, Ching-Hung
;
Kau, Lih-Jen
;
Lin, Yuan-Pei
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電控工程研究所
;
Institute of Electrical and Control Engineering
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-theart lossless predictors and coders will be given to highlight the advantages of the proposed novel approach.
Least squares-adapted edge-look-ahead prediction with run-length encodings for lossless compression of images
(2008)
Kau, Lih-Jen
;
Lin, Yuan-Pei
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Many coding methods are more efficient with certain types of images than others. In particular, run-length coding is very useful for coding areas of little changes. Adaptive predictive coding achieves high coding efficiency for fast changing areas like edges. In this paper, we propose a switching coding scheme that will combine the advantages of both Run-length and Adaptive Linear Predictive coding (RALP) for lossless compression of images. For pixels in slowly varying areas, run-length coding is used; otherwise LS (least square)-adapted predictive coding is used. Instead of performing LS adaptation in a pixel-by-pixel manner, we adapt the predictor coefficients only when an edge is detected so that the computational complexity can be significantly reduced. For this, we propose an edge detector using only causal pixels. This way, the predictor can look ahead if the coding pixel is around an edge and initiate the LS adaptation in advance to prevent the occurrence of a large prediction error. With the proposed switching structure, very good prediction results can be obtained in both slowly varying areas and pixels around boundaries as we will see in the experiments.
Least squares-based lossless image coding with edge-look-ahead
(2006)
Kau, Lih-Jen
;
Lin, Yuan-Pei
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In predictive image coding, the least squares (LS)-based adaptive predictor is noted as an efficient method to improve prediction result around edges. However pixel-by-pixel optimization of the predictor coefficients leads to a high coding complexity. To reduce computational complexity, we activate the LS optimization process only when the coding pixel is around an edge or when the prediction error is large. We propose a simple yet effective edge detector using only causal pixels. The system can look ahead to determine if the coding pixel is around an edge and initiate the LS adaptation to prevent the occurrence of a large prediction error. Our experiments show that the proposed approach can achieve a noticeable reduction in complexity with only a minor degradation in the prediction results.
Least-squares-based switching structure for lossless image coding
(2007-07-01)
Kau, Lih-Jen
;
Lin, Yuan-Pei
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Many coding methods are more efficient with some images than others. In particular, run-length coding is very useful for coding areas of little changes. Adaptive predictive coding achieves high coding efficiency for fast changing areas like edges. In this paper, we propose a switching coding scheme that will combine the advantages of both run-length and adaptive linear predictive coding. For pixels in slowly varying areas, run-length coding is used; otherwise least-squares (LS)-adaptive predictive coding is used. Instead of performing LS adaptation in a pixel-by-pixel manner, we adapt the predictor coefficients only when an edge is detected so that the computational complexity can be significantly reduced. For this, we use a simple yet effective edge detector using only causal pixels. This way, the proposed system can look ahead to determine if the coding pixel is around an edge and initiate the LS adaptation in advance to prevent the occurrence of a large prediction error. With the proposed switching structure, very good prediction results can be obtained in both slowly varying areas and pixels around boundaries. Furthermore, only causal pixels are used for estimating the coding pixels in the proposed encoder; no additional side information needs to be transmitted. Extensive experiments as well as comparisons to existing state-of-the-art predictors and coders will be given to demonstrate its usefulness.