| Summary: | 博士 === 國立交通大學 === 電機與控制工程系所 === 97 === Lossless image coding is required by many applications, such as medical imaging, remote sensing, and image archiving. It has remained a major challenge to source coding community for the difficulty of removing statistical redundancy effectively and efficiently. Therefore, many approaches have been proposed for lossless compression of images. Among proposed approaches, some of which are based on reversible transform coding, like integer wavelet transformation. However, we find in literatures that the results obtained by using transform coding are typically inferior to that of obtained by predictively encoded techniques with context modeling in spatial domain.
In this dissertation, an introduction on recent advances in lossless image coding will be given. Moreover, we will propose an approach based on linear predictive coding with least-squares (LS) optimization for the adaptation of predictor coefficients. The LS-based adaptive predictor, for its edge-directed characteristic, has been shown to be useful for the prediction of pixels around boundaries. 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 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. Furthermore, only causal pixels are used for estimating the coding pixels in the proposed encoder; no additional side information needs to be transmitted. As we will see later in the experiments, a very good trade-off between prediction results and the computational complexity can be obtained with the proposed approach. Besides, extensive experiments as well as comparisons to existing state-of-the-art predictors and coders will be given to demonstrate the usefulness of the proposed approach.
In addition to the proposed edge-look-ahead approach, we find a large prediction error can usually take place for pixels around boundaries. Therefore, we also propose in this dissertation a novel concept of using control technologies to improve prediction result for pixels around boundaries. This idea comes from the fact that the purpose of a control system is to follow the input command as precisely as possible, which has the same objective with predictive coding. Moreover, an edge or a boundary can be regarded as a step command in control system. The above observations lead to the idea of solving this problem using control technologies. To realize this idea, we also implement an adaptive predictor using Takagi-Sugeno fuzzy neural network (TS-FNN). Moreover, the widely used proportional controller in control theory is applied implicitly in the consequent part of the network as a compensator to enhance the prediction result around edges. The effectiveness of the proposed novel approach, though not very conspicuous at present, can be further improved if a more sophisticated compensator is applied, and what's more, we have brought up an idea of solving this problem in a quite different aspect for lossless compression of images.
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