Browsing by Author "Kau, LJ"

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A switching predictor for lossless image coding
(2003)
Kau, LJ
;
Lin, YP
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, we propose a switching adaptive predictor (SWAP) with automatic context modeling for lossless image coding. In the SWAP system, two predictors are used. For areas with edges, estimates of coding pixels are obtained using texture context matching (TCM). For all other areas, an adaptive neural predictor (ANP) is used. The SWAP encoder switches between the two predictors ANP and TCM depending on the neighborhood of the coding pixel. The switching predictor allows statistical redundancy to be removed effectively. On the other hand, it is known that prediction can be further refined using error compensation. For this, we propose the use of a modified fuzzy clustering, which leads to a modeling of errors that adapts itself to the input statistics. Experiments show that the proposed context clustering is very useful in modeling error for prediction refinement. Comparisons of the proposed system to existing state-of-the-art predictive coders will be given to demonstrate its coding efficiency.
Adaptive lossless image coding using least squares optimization with edge-look-ahead
(2005-11-01)
Kau, LJ
;
Lin, YP
;
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.
Adaptive predictor with dynamic fuzzy K-means clustering for lossless image coding
(2003)
Kau, LJ
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposed a nonlinear predictor ADFK (Adaptive predictor with Dynamic Fuzzy K-means clustering error feedback) for lossless image coding based on multi-layered perceptrons. Since real images are usually nonstationary, a fixed predictor is not adequate to handle the varying statistics of input images. Using back propagation learning with causal neighbors of the coding pixel as training patterns to update network weights continuously, ADFK is made adaptive on the fly. Furthermore, prediction error is further refined in ADFK by applying error compensation different to compound context error modeling used in CALIC [1] based on dynamic codebook design with adaptive fuzzy k-means clustering algorithm. Compensated errors are then entropy encoded using conditional arithmetic coding based on error strength estimation. The proposed compensation mechanism is proved to be very useful through experiments by further improving the bit rates in an average amount of about 0.2bpp in test images. Success in the use of proposed predictor is demonstrated through the reduction in the entropy and actual bit rate of the differential error signal as compared to that of existing linear and nonlinear predictors.
Lossless image coding using a switching predictor with run-length encodings
(2004)
Kau, LJ
;
Lin, YP
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, we propose a switching adaptive predictor (FSWAP) with run-length encodings for lossless image coding. The proposed FSWAP system has two operation modes, run mode and regular mode. If the members in the texture context of the coding pixel have identical grey values, the run mode will be used; otherwise regular mode is used. The run mode using run-length coding with an arithmetic coder is very useful for image with flat regions. The regular mode borrows the switching predictor structure in SWAP [1] with some modifications. The SWAP coder, updating network weights on the fly and making automatic fuzzy context modeling, has been shown to provide very good results [1]. To make the proposed system more feasible for applications of limited resources, the dimension of ANP in SWAP [1] is simplified and the automatic fuzzy context modeling is replaced by a fixed context clustering. Experiments show that the simplified context clustering is very useful in modeling error for prediction refinement. Furthermore, the execution time of FSWAP can be accelerated with minor degradation in the bit rates associated with the modifications. Comparisons of the proposed system to existing state-of-the-art predictive coders will be given to demonstrate its coding efficiency.
Lossless image coding using adaptive predictor with automatic context modeling
(2003)
Kau, LJ
;
電控工程研究所
;
Institute of Electrical and Control Engineering
An adaptive predictor with automatic context modeling (APACM) is proposed for lossless image coding in this paper. The main prediction stage of APACM is a three-layered back-propagation neural network. Due to the nonstationary property of real images, a fixed predictor is not adequate to deal with the varying statistics of input images. Using causal neighbors of the coding pixel as training patterns, network weights of APACM are adapted on the fly. For error compensation mechanism, context modeling is made automatic using vector quantization based on a modified UFCL (Unsupervised Fuzzy Competitive Learning). Refined prediction errors are then entropy encoded using conditional arithmetic coding to produce the code stream. Experiments show that proposed APACM can remove redundancy between image pixels efficiently. Comparisons of the proposed system to existing state-of-the-art predictors will be given to demonstrate its usefulness.