For inter frame coding, the motion-compensated residual takes a large proportion of the total bits and the efficiency of the followed transform greatly affects the compression performance. In this paper, we propose an...
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ISBN:
(纸本)9783642156953
For inter frame coding, the motion-compensated residual takes a large proportion of the total bits and the efficiency of the followed transform greatly affects the compression performance. In this paper, we propose an adaptive transform scheme to further exploit the non-local correlation for the motion-compensated residual. For a video sequence, there are usually repeating similar contents, especially between adjacent frames, known as temporal redundancy. We then use these content-similar blocks of the coding block, which most probably reflect the characteristic of the coding block, to train the adaptive transform. The predicted block together with the boundary reconstructed pixels of the coding block forms the target patch and is used to guide the searching of similar blocks. By fully exploring the correlation of abundant similar blocks, the proposed scheme achieves 0.1 similar to 0.5 dB gain in term of PSNR at high bit rate over the state-of-the-art scheme. For Mobile and BQSquare, 1dB gain is obtained at high bit rate.
This paper presents a lossless inter geometry coder of voxelized point clouds. We build upon our previous work, extending the contexts used in the arithmetic coding to include voxels from a reference point cloud, henc...
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ISBN:
(纸本)9781728163956
This paper presents a lossless inter geometry coder of voxelized point clouds. We build upon our previous work, extending the contexts used in the arithmetic coding to include voxels from a reference point cloud, hence the name, 4D contexts. We show that considering both 3D and 4D contexts leads to a substantial gain compared to considering each one on its own, and we also propose a fast decision method to avoid encoding each slice with both contexts. The proposed codec has the same complexity as our previous intra codec, but it shows an equal or superior performance for all point clouds tested. Results show that the proposed method outperforms all intra and inter state-of-the-art coders on the public available datasets tested.
Recently, learning-based in-loop filtering has attracted lots of attention. State-of-the-art works generally deploy computationally expensive, large-scale neural networks, which is unfriendly to practical applications...
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ISBN:
(数字)9781665496209
ISBN:
(纸本)9781665496209
Recently, learning-based in-loop filtering has attracted lots of attention. State-of-the-art works generally deploy computationally expensive, large-scale neural networks, which is unfriendly to practical applications. Besides, since these models are generally pre-trained using a limited dataset and applied to various videos, they may fail in video contents excluded in the training dataset. To address these issues, this paper develops a Guide CNN in-loop filtering framework to obtain the restored signal. Our basic idea is to construct a subspace and use the projection of the original signal into this subspace to approximate the original signal itself. Specifically, we employ CNN to transform the degraded signal into M subsignals to construct the optimal subspace since the training of CNN is essentially an optimization procedure. Furthermore, unlike existing CNN models that process all blocks uniformly, our method leverages a quadtree structure to implement the Guided CNN through R-D optimization. As such, the best partition to Guided CNN can be determined. We exemplify the proposed method in AV1 codec. Experimental results show that the Guided CNN framework achieves 2.19% and 1.31% BD-Rate gains over the AV1 anchor in intra and intercoding mode, respectively, while the normal CNN achieves only 1.64% and 1.04%.
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