A data embedding solution in HEVC videos is proposed by modifying the partitioning of coding units (CUs). The partitions of a CU are first represented as a sequence of binary flags. The flags pertaining to 16 x 16 sub...
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A data embedding solution in HEVC videos is proposed by modifying the partitioning of coding units (CUs). The partitions of a CU are first represented as a sequence of binary flags. The flags pertaining to 16 x 16 sub CUs are used as a cover for data embedding, where 6 or 4 message bits are embedded per CU. The data embedding algorithm guarantees that a maximum of one partition is modified per message segment, therefore, in a given CU, either 0, 1 or 2 partitions are modified. The proposed solution is assessed in terms of message payload, number of modified partitions, loss in video quality as indicated by the PSNR results, mean objective scores and excessive bitrate. The proposed solution can embed messages with up to an average payload of 32.6 kbit/s with a corresponding average distortion of <0.5 dB. Comparisons with existing solutions reveal that the proposed solution maintains similar message payloads with less modifications of CU partitioning and at the same time resulting in less distortions for the cover video.
The latest Joint Video Exploration Team employs quad-tree plus binary-tree (QTBT) block partitioning structure, which can improve coding performance significantly than High Efficiency Video coding with hugely increase...
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The latest Joint Video Exploration Team employs quad-tree plus binary-tree (QTBT) block partitioning structure, which can improve coding performance significantly than High Efficiency Video coding with hugely increased encoding complexity. To address this issue, we propose a novel fast QTBT partition method through a convolutional neural network (CNN). Specifically, the proposed algorithm uses CNN to predict the QTBT partition depth range of 32 x 32 block directly according to the inherent texture richness of the image, rather than to judge split or not at each depth level. For training optimization, we introduce a misclassification penalty term combined with L2 HingeLoss function, which can further boost the classification accuracy. Experimental results demonstrate the effectiveness of our proposed method;our rate-distortion maintaining setting can achieve 42.33% complexity reduction with just 0.69% bitrate increase. Our low complexity setting can achieve 62.08% complexity reduction with 2.04% bitrate increase.
Being a scalable extension of the High Efficiency Video coding (HEVC), Scalable High Efficiency Video coding (SHVC) standard makes it possible to perform scalable encodings. It produces a single binary stream over sev...
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Being a scalable extension of the High Efficiency Video coding (HEVC), Scalable High Efficiency Video coding (SHVC) standard makes it possible to perform scalable encodings. It produces a single binary stream over several layers built from the same video at different scales of resolutions, frequencies, qualities, pixel depths, or color dynamics. However, SHVC is dedicated to the scalable compression of conventional 2D videos whose only component is the texture image, while a compact and highly scalable representation of depth data is also required in several innovative current and future applications. Finalized in February 2015, 3D High Efficiency Video coding (3D-HEVC) was introduced as a standard dedicated to depth maps compression. But, it does not allow scalable compression of these latter. We are then faced with 3D-HEVC, a standard adapted to depth maps but not scalable, and SHVC, a standard for scalable compression but not adapted to depth maps. In this paper, we aim to propose our custom SHVC in order to handle the signal-to-noise ratio (SNR) scalable compression of depth maps. This codec consists in limiting SNR scalability to sharp depth discontinuities and their neighborhoods. Increasing quantization parameters values are then conditionally used for the quantization of the coding units transform coefficients as we move away from the contours. Our tailored SHVC codec, when compared to the unmodified SHVC and a 3D-HEVC-based state-of-the-art method, significantly improves the distortion vs. rate performance for benchmark depth maps sequences.
The High Efficiency Video coding (HEVC) will be the next generation video standard, which adopts several new techniques to achieve better coding efficiency compared to the current H.264. One significant progress is th...
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ISBN:
(纸本)9781479912919
The High Efficiency Video coding (HEVC) will be the next generation video standard, which adopts several new techniques to achieve better coding efficiency compared to the current H.264. One significant progress is the way of coding architecture partitioning. HEVC employs a recursive quadtree to get the best partitioning, which means each 64x64 pixel area is analyzed in different size coding units (CU). Due to the large number of available partitions, this new partitioning method greatly increases the encoding time. In this paper, one fast CU partitioning algorithm based on Rate Distortion (RD) threshold to save much encoding time is proposed and the test shows that the average quality loss is less than 0.5%.
HEVC is planned to provide better coding efficiency. However, this efficiency comes with an important computational complexity. Many works are undergoing to decrease the complexity of the encoder. Nevertheless, the an...
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ISBN:
(纸本)9781467366373
HEVC is planned to provide better coding efficiency. However, this efficiency comes with an important computational complexity. Many works are undergoing to decrease the complexity of the encoder. Nevertheless, the analysis of the encoder' decision is necessary to have an idea about partitioning mode of the CUs in terms of size and type. This paper presents statistical analyses of coding units (CU) chosen by the encoder. These statistics are followed by a texture analysis. A texture parameter was established for the different videos encoded using HEVC. The analysis of the texture is made using the Sobel filter. The results show that the percentage of choosing the coding units depends directly on the video characteristics. In fact, for textured videos, having a lower texture parameter, there is a need to process with smallest coding units. This work is as milestone for proposing algorithms based on video characteristics to perform fast partitioning.
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