- Read more about Graph-based Transform based on 3D Convolutional Neural Network for Intra-Prediction of Imaging Data
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This paper presents a novel class of Graph-based Transform based on 3D convolutional neural networks (GBT-CNN) within the context of block-based predictive transform coding of imaging data. The proposed GBT-CNN uses a 3D convolutional neural network (3D-CNN) to predict the graph information needed to compute the transform and its inverse, thus reducing the signalling cost to reconstruct the data after transformation.
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- Read more about Joint Rate Distortion Optimization with CNN-based In-Loop Filter For Hybrid Video Coding
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- Read more about Stochastic Model of Block Segmentation Based on Improper Quadtree and Optimal Code under the Bayes Criterion
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Most previous studies on lossless image compression have focused on improving preprocessing functions to reduce the redundancy of pixel values in real images. However, we assumed stochastic generative models directly on pixel values and focused on achieving the theoretical limit of the assumed models. In this study, we proposed a stochastic model based on improper quadtrees. We theoretically derive the optimal code for the proposed model under the Bayes criterion. In general, Bayes-optimal codes require an exponential order of calculation with respect to the data lengths.
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- Read more about Deep Correlated Image Set Compression Based on Distributed Source Coding and Multi-Scale Fusion
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In this paper, we present a deep correlated image set compression scheme based on Distributed Source Coding(DSC) and multi-scale image fusion. As there exists strong correlation among images in a similar image set, we propose to utilize such correlation to generate side information at decoder side for each image in the set. Specifically, a reference structure of the image set is generated by building a minimum spanning tree according to the similarity between two images at encoder.
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- Read more about A low-complexity destriping method for lossless compression of remote-sensing data
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Existing methods for cross-component prediction focus on the chroma intra prediction but neglect the chroma prediction across multiple frames. In this paper, we propose a novel compression framework that leverages chroma frame sampling and inter-frame chroma prediction to address this problem. Specifically, chroma frame sampling discards the chroma components in inter-predicted frames to further reduce the bit consumption, whereas inter-frame chroma prediction recovers the discarded chroma components with optical flow estimation and post-training optimization for a guaranteed fidelity.
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- Read more about Cross-component Sample Adaptive Offset
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This paper proposes one new in-loop filtering technique cross-component sample adaptive offset (CCSAO) for further coding efficiency improvement beyond Versatile Video Coding (VVC). The CCSAO reduces the sample distortion by 1) utilizing the strong correlation between luma and chroma components to classify the reconstructed samples into different categories and 2) deriving one offset for each category and adding the offset to the samples in the category. The offset of each category is properly derived at encoder and signaled to decoder.
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- Read more about An Improvement to Intra Block Copy in VVC with Reference Area Redefinition
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DCC2022_xu.pdf
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- Read more about Concurrent Encryption and Lossless Compression using Inversion Ranks
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dcc_202.pdf
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- Read more about Analysis of Lossless Compressors Applied to Integer and Floating-Point Astronomical Data
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