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Register a new userFast Block Structure Determination in AV1-based Multiple Resolutions Video Encoding
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Added by
Bichuan Guo
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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The widely used adaptive HTTP streaming requires an efficient algorithm to encode the same video to different resolutions. In this paper, we propose a fast block structure determination algorithm based on the AV1 codec that accelerates high resolution encoding, which is the bottle-neck of multiple resolutions encoding. The block structure similarity across resolutions is modeled by the fineness of frame detail and scale of object motions, this enables us to accelerate high resolution encoding based on low resolution encoding results. The average depth of a blocks co-located neighborhood is used to decide early termination in the RDO process. Encoding results show that our proposed algorithm reduces encoding time by 30.1%-36.8%, while keeping BD-rate low at 0.71%-1.04%. Comparing to the state-of-the-art, our method halves performance loss without sacrificing time savings.
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Due to differences in frame structure, existing multi-rate video encoding algorithms cannot be directly adapted to encoders utilizing special reference frames such as AV1 without introducing substantial rate-distortion loss. To tackle this problem, we propose a novel bayesian block structure inference model inspired by a modification to an HEVC-based algorithm. It estimates the posterior probabilistic distributions of block partitioning, and adapts early terminations in the RDO procedure accordingly. Experimental results show that the proposed method provides flexibility for controlling the tradeoff between speed and coding efficiency, and can achieve an average time saving of 36.1% (up to 50.6%) with negligible bitrate cost.
This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in intra slices. An encoding approach based on Convolutional Neural Networks is explored to partly substitute classical heuristics-based encoder speed-ups by a systematic and automatic process. The solution allows controlling the trade-off between complexity and coding gains, in intra slices, with one single parameter. This algorithm was proposed at the Call for Proposals of the Joint Video Exploration Team (JVET) on video compression with capability beyond HEVC. In All Intra configuration, for a given allowed topology of splits, a speed-up of $times 2$ is obtained without BD-rate loss, or a speed-up above $times 4$ with a loss below 1% in BD-rate.
Image steganography is the art of hiding information into a cover image. This paper presents a novel technique for Image steganography based on Block-DCT, where DCT is used to transform original image (cover image) blocks from spatial domain to frequency domain. Firstly a gray level image of size M x N is divided into no joint 8 x 8 blocks and a two dimensional Discrete Cosine Transform (2-d DCT) is performed on each of the P = MN / 64 blocks. Then Huffman encoding is also performed on the secret messages/images before embedding and each bit of Huffman code of secret message/image is embedded in the frequency domain by altering the least significant bit of each of the DCT coefficients of cover image blocks. The experimental results show that the algorithm has a high capacity and a good invisibility. Moreover PSNR of cover image with stego-image shows the better results in comparison with other existing steganography approaches. Furthermore, satisfactory security is maintained since the secret message/image cannot be extracted without knowing decoding rules and Huffman table.
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