Evolutionary methods based on genetic programming (GP) enable dynamic algorithm generation, and have been successfully applied to many areas such as plant control, robot control, and stock market prediction. However, ...
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ISBN:
(纸本)9781424456536
Evolutionary methods based on genetic programming (GP) enable dynamic algorithm generation, and have been successfully applied to many areas such as plant control, robot control, and stock market prediction. However, conventional image/video coding methods such as JPEG and H.264 all use fixed (non-dynamic) algorithms without exception. In this article, we introduce a GP-based image predictor that is specifically evolved for each input image. Experimental results demonstrate 2.9 % less entropy (overhead included) than CALIC's gradient adjusted predictor.
This paper describes a lossless compression method for medical images that produces an embedded bit-stream, allowing progressive lossy-to-lossless decoding with L-infinity oriented rate-distortion. The experimental re...
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ISBN:
(纸本)9781424423538
This paper describes a lossless compression method for medical images that produces an embedded bit-stream, allowing progressive lossy-to-lossless decoding with L-infinity oriented rate-distortion. The experimental results show that the proposed technique produces better average lossless compression results than several other compression methods, including JPEG2000, JPEG-LS and JBIG, in a publicly available medical image database containing images from several modalities.
The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete...
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The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs) is proposed. In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN, and since the output function of DT-CNN works as a multi-level quantization function, our method composes the integer lifting scheme for lossless image coding. Moreover, the nonlinear interpolative dynamics by A-template is used effectively compared with conventional CNN imagecoding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting methods using linear filters.
In the paper modeling stage of a simple and efficient lossless image coding technique intended for use in real-time hardware coding system is described The method is based on predictors blending approach, which is the...
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ISBN:
(纸本)9789537044060
In the paper modeling stage of a simple and efficient lossless image coding technique intended for use in real-time hardware coding system is described The method is based on predictors blending approach, which is the kernel of some the best current losslesscoding methods. Additionally, prediction blending is well suited for parallel implementations. The predictor blending is combined with context coding, and bias removal algorithm, the latter one implements both CALIC, and LOCO-I approaches. Experiments show that indeed, the new technique has better performance than the widely known older methods.
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 effi...
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ISBN:
(纸本)9781424414833
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.
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 e...
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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.
Significant lossless compression results of color map images have been obtained by dividing the color maps into layers and by compressing the binary layers separately using an optimized context tree model that exploit...
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Significant lossless compression results of color map images have been obtained by dividing the color maps into layers and by compressing the binary layers separately using an optimized context tree model that exploits interlayer dependencies. Even though the use of a binary alphabet simplifies the context tree construction and exploits spatial dependencies efficiently, it is expected that an equivalent or better result would be obtained by operating directly on the color image without layer separation. In this paper, we extend the previous context-tree-based method to operate on color values instead of binary layers. We first generate an n-ary context tree by constructing a complete tree up to a predefined depth, and then prune out nodes that do not provide compression improvements. Experiments show that the proposed method outperforms existing methods for a large set of different color map images.
Natural, continuous tone images have a very important property of high correlation of adjacent pixels. This property is cleverly exploited in losslessimage compression where, prior to the statistical modeling and ent...
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ISBN:
(纸本)9539676967
Natural, continuous tone images have a very important property of high correlation of adjacent pixels. This property is cleverly exploited in losslessimage compression where, prior to the statistical modeling and entropy coding step, predictive coding as decorrelation toot is used. The use of prediction for current pixel also reduces the model cost of applied statistical model for entropy coding. Linear prediction, where predicted value is linear function of previously encoded pixels (causal template), has proven to give very good results as a decorrelation tool in losslessimage compression. In this work we concetrate on adaptive linear predictors used in lossless image coding and propose a new linear prediction method.
A new lossless intra coding method based on sample-by-sample differential pulse code modulation (DPCM) is presented as an enhancement of the H.264[MPEG-4 AVC standard. The H.264/AVC design includes a multidirectional ...
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A new lossless intra coding method based on sample-by-sample differential pulse code modulation (DPCM) is presented as an enhancement of the H.264[MPEG-4 AVC standard. The H.264/AVC design includes a multidirectional spatial prediction method to reduce spatial redundancy by using neighboring samples as a prediction for the samples in a block of data to be encoded. In the new lossless;intra coding method, the spatial prediction is performed based on samplewise DPCM instead of in the block-based manner used in the current H.264/AVC standard, while the block structure is retained for the residual difference entropy coding process. We show that the new method, based on samplewise DPCM, does not have a major complexity penalty, despite its apparent pipeline dependencies. Experiments show that the new lossless intra coding method reduces the bit rate by approximately 12% in comparison with the lossless;intra coding method previously included in the H.264/AVC standard. As a result, the new method is currently being adopted into the H.264/AVC standard in a new enhancement project.
This paper presents a new three-dimensional (3-D) wavelet-based scalable losslesscoding scheme for compression of volumetric medical images. Aiming to improve the productivity of radiologists and the cost-effectivene...
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This paper presents a new three-dimensional (3-D) wavelet-based scalable losslesscoding scheme for compression of volumetric medical images. Aiming to improve the productivity of radiologists and the cost-effectiveness of the system, we strive to achieve high decoder throughput, random access to coded data volume, progressive transmission, and high compression ratio in a balanced design approach. These desirable functionalities are realized by a modified 3-D dyadic wavelet transform tailored to volumetric medical images and an optimized Rice code of very low complexity.
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