This paper presents a new lossless color image compression algorithm, based on the hierarchical prediction and context-adaptivearithmeticcoding. For the lossless compression of an RGB image, it is first decorrelated...
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This paper presents a new lossless color image compression algorithm, based on the hierarchical prediction and context-adaptivearithmeticcoding. For the lossless compression of an RGB image, it is first decorrelated by a reversible color transform and then Y component is encoded by a conventional lossless grayscale image compression method. For encoding the chrominance images, we develop a hierarchical scheme that enables the use of upper, left, and lower pixels for the pixel prediction, whereas the conventional raster scan prediction methods use upper and left pixels. An appropriate context model for the prediction error is also defined and the arithmeticcoding is applied to the error signal corresponding to each context. For several sets of images, it is shown that the proposed method further reduces the bit rates compared with JPEG2000 and JPEG-XR.
The three main pillars for lossless image compression are prediction, prediction residual correction and entropy coding. In this work, we propose a lossless image compression algorithm which utilizes local information...
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ISBN:
(纸本)9781728129402
The three main pillars for lossless image compression are prediction, prediction residual correction and entropy coding. In this work, we propose a lossless image compression algorithm which utilizes local information to dynamically switch between different prediction techniques. Moreover, we improve on traditional prediction techniques and modify them into diagonal GAP and dynamic window weighted linear prediction. Finally, since the coding efficiency is directly proportional to the context being chosen if contextarithmeticcoding is applied, we propose a robust context model which utilizes local information to get better coding efficiency.
Conventional lossless compression techniques that use look up table method tend to be inefficient. We propose a deep context model, named DecMac, which combines a three-layer LSTM with adaptivearithmeticcoding for l...
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ISBN:
(纸本)9781538678220
Conventional lossless compression techniques that use look up table method tend to be inefficient. We propose a deep context model, named DecMac, which combines a three-layer LSTM with adaptivearithmeticcoding for lossless compression. In order to capture much more context information for better predicting, we introduce a cycle connection to preserve the end of hidden states and reuse it as the initial states for the next batch. We evaluate our method on the text compression task, resulting in averaged 25 % compressed size reduction over the state of the art PAQ, and averaged 45 % reduction over GZIP and ZIP.
Depth maps are becoming increasingly important in the context of emerging video coding and processing applications. Depth images represent the scene surface and are characterized by areas of smoothly varying grey leve...
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ISBN:
(纸本)9781467372589
Depth maps are becoming increasingly important in the context of emerging video coding and processing applications. Depth images represent the scene surface and are characterized by areas of smoothly varying grey levels separated by sharp edges at the position of object boundaries. To enable high quality view rendering at the receiver side, preservation of these characteristics is important. Lossless coding enables avoiding rendering artifacts in synthesized views due to depth compression artifacts. In this paper, we propose a binary tree based lossless depth coding scheme that arranges the residual frame into integer or binary residual bitmap. High spatial correlation in depth residual frame is exploited by creating large homogeneous blocks of adaptive size, which are then coded as a unit using context based arithmeticcoding. On the standard 3D video sequences, the proposed lossless depth coding has achieved compression ratio in the range of 20 to 80.
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