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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Hashidume, Yoshihiko Morikawa, Yoshitaka Okayama Univ Grad Sch Nat Sci & Technol Okayama Japan

ISBN: (纸本)9784907764289

For prediction-based lossless image coding, the coding performance depends largely on the efficiency of predictors. In general, mmse predictors are well used, but these predictors suffer from large errors at edges. In response, the authors have proposed minimum mean absolute error (mmae) predictors which are less sensitive to edges. Mmae predictors provide accurate prediction and entropy of prediction errors is reduced. In this paper we infer prediction errors based on mmae and mmse predictors can be modeled by the Laplacian and Gaussian function, respectively, and conclude mmae predictors are superior to mmse predictors in terms of coding performance.

关键词： lossless image coding mmae predictor adaptive prediction context modeling error modeling

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lossless image coding based on fractals

Lossless image coding based on fractals

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7th IASTED International Conference on Signal and image Processing

作者： De Schrijver, D De Sutter, R Lambert, P Van de Walle, R State Univ Ghent IBBT Dept Elect & Informat Syst Multimedia Lab B-9000 Ghent Belgium

ISBN: (纸本)0889865167

In some kind of applications, such as in space image research, every detail of an image can be important. To send the information more efficiently over a network, the image will be compressed and in the former applications that must be done in a lossless manner. After decoding, a bit-equivalent version is obtained at the receiver. Therefore, the JPEG-LS standard can be used. However, this paper will introduce another approach for lossless image coding. At the encoder, we introduce a quantization error and which will be eliminated at the decoder by using an iterative procedure. The outcome of this procedure is an attractor (or fractal) which has to be the original image. We discuss different implementations of a fractal lossless encoder and come to the conclusion that we can achieve a compression that is comparable to JPEG-LS, but not better at the moment.

关键词： lossless image coding fractals attractor coding JPEG-LS

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Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for lossless image coding

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IEEE TRANSACTIONS ON image PROCESSING 2014年第2期23卷 541-554页

作者： Dai, Wenrui Xiong, Hongkai Wang, Jia Zheng, Yuan F. Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.

关键词： Structured set prediction max-margin Markov networks lossless image coding discriminative model

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Adaptive lossless image coding using least squares optimization with edge-look-ahead

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2005年第11期52卷 751-755页

作者： Kau, LJ Lin, YP Natl Chiao Tung Univ Dept Elect & Control Engn Hsinchu 30010 Taiwan

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.

关键词： adaptive prediction context modeling edge detection entropy coding least squares (LS) optimization lossless image coding

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Least-squares-based switching structure for lossless image coding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2007年第7期54卷 1529-1541页

作者： Kau, Lih-Jen Lin, Yuan-Pei Natl Chiao Tung Univ Dept Elect & Control Engn Hsinchu 300 Taiwan Dahan Inst Technol Dept Comp & Commun Engn Hualien 971 Taiwan

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.

关键词： adaptive prediction context modeling edge detection entropy coding least-squares (LS) optimization lossless image coding run-length encodings

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Minimum mean absolute error predictors for lossless image coding

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2008年第6期E91D卷 1783-1792页

作者： Hashidume, Yoshihiko Morikawa, Yoshitaka Maki, Shuichi Okayama Univ Grad Sch Nat Sci & Technol Okayama 7008530 Japan Hitachi Ind Equipment Syst Co Ltd R&D Ctr Tokyo 1010022 Japan

In this paper, we investigate minimum mean absolute error (mmae) predictors for lossless image coding. In some prediction-based lossless image coding systems, coding performance depends largely on the efficiency of predictors. In this case, minimum mean square error (mmse) predictors are often used. Generally speaking, these predictors have a problem that outliers departing very far from a regression line are conspicuous enough to obscure inliers. That is, in image compression, large prediction errors near edges cause the degradation of the prediction accuracy of flat areas. On the other hand, mmae predictors are less sensitive to edges and provide more accurate prediction for flat areas than mmse predictors. At the same time, the prediction accuracy of edge areas is brought down. However, the entropy of the prediction errors based on mmae predictors is reduced compared with that of mmse predictors because general images mainly consist of flat areas. In this study, we adopt the Laplacian and the Gaussian function models for prediction errors based on mmae and mmse predictors, respectively, and show that mmae predictors outperform conventional mmse-based predictors including weighted mmse predictors in terms of coding performance.

关键词： lossless image coding minimum mean absolute error l(1)-problem linear programming mmae predictor adaptive prediction context modeling error modeling

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Performance analysis of wavelets in embedded zerotree-based lossless image coding schemes

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1999年第3期47卷 884-889页

作者： Ramaswamy, VN Ranganathan, N Namuduri, KR Lucent Technol Bell Labs Holmdel NJ 07733 USA Clark Atlanta Univ Ctr Theoret Studies Phys Syst Atlanta GA 30314 USA

In this correspondence, we present a modification to the scanning approach in the set partitioning algorithm proposed by Said and Pearlman to exploit the correlation in a local neighborhood. The wavelet filters are characterized based on the wavelet coefficients obtained after the wavelet transform. Two new criteria are proposed for evaluating the performance of wavelets in lossless image compression applications: cumulative zerotree count and monotone spectral ordering of subbands produced after wavelet transform in a multiresolution scheme. Several wavelet filters are evaluated to test the evaluation criteria. The experimental results are presented to justify the proposed performance criteria.

关键词： embedded zerotree wavelet lossless image coding performance analysis subband coding

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A linear edge model and its application in lossless image coding

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SIGNAL PROCESSING-image COMMUNICATION 2004年第10期19卷 955-958页

作者： Wang, HY Zhang, D Shandong Univ Sch Informat Sci & Engn Jinan 250100 Shandong Peoples R China Hong Kong Polytech Univ Dept Comp Ctr Multimedia Signal Proc Kowloon Hong Kong Peoples R China

A linear edge model for the prediction of edge pixel values is first proposed and a gradient-adjusted predictor based on this model for context-based lossless image coding is then developed. Theoretical analysis and experiment results show that the performance of the proposed predictor is better than that of the state-of-the-art predictors for most test images. (C) 2004 Elsevier B.V. All rights reserved.

关键词： lossless image coding image edge model gradient-adjusted predictor linear edge

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Nonlocal Context Modeling and Adaptive Prediction for lossless image coding

Nonlocal Context Modeling and Adaptive Prediction for Lossle...

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Picture coding Symposium (PCS)

作者： Chen, Hsin-Hui Ding, Jian-Jiun Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan

ISBN: (纸本)9781479902941

Properly designed context models can increase the compression gain. In this paper, we propose a new lossless image coding scheme with two proposed algorithms: nonlocal context modeling and adaptive prediction (NCMAP). Since structural self-similarity often exists in natural images, we use the probability to measure the similarity between the powers of prediction errors for the pixels to be coded. Furthermore, the spatial distance and the intensity range are also considered for context generation. Moreover, a prediction scheme that adaptively combines the weighted edge-directed prediction (WEDP) and the nonlocal predictor (NLP) is also proposed. With the proposed context generating and prediction strategies, better compression performances can be achieved. Simulations show that the proposed scheme outperforms existing methods for lossless image compression.

关键词： Context modeling lossless image coding adaptive prediction strength range lossless compression lossless image compression Error of prediction Power (Psychology) pixel

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Enhancement of CNN-based Probability Modeling by Locally Trained Adaptive Prediction for Efficient lossless image coding

Enhancement of CNN-based Probability Modeling by Locally Tra...

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Picture coding Symposium (PCS)

作者： Kaji, Keisuke Kita, Yasuyo Matsuda, Ichiro Itoh, Susumu Kameda, Yusuke Tokyo Univ Sci Fac Sci & Technol 2641 Yamazaki Noda Chiba 2788510 Japan Sophia Univ Tokyo Japan

ISBN: (纸本)9781665492577

An autoregressive image generative model that estimates the conditional probability distributions of image signals pel-by-pel is a promising tool for lossless image coding. In this paper, a generative model based on a convolutional neural network (CNN) was combined with a locally trained adaptive predictor to improve its accuracy. Furthermore, sets of parameters that adjust the estimated probability distribution were numerically optimized for each image to minimize the resulting coding rate. Simulation results indicate that the proposed method improves the coding efficiency obtained by the CNN-based model for most of the tested images.

关键词： lossless image coding image generative model convolutional neural network adaptive prediction

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