The Experts below are selected from a list of 76857 Experts worldwide ranked by ideXlab platform
Wai-kuen Cham - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Encoding Algorithm for Vector Quantization of Images
Signal Processing, 1992Co-Authors: Kwok-tung Lo, Wai-kuen ChamAbstract:ABSTRACT: In vector quantization of images, the main problem is its computation complexity in the Encoding Process. In this work, a sub-codebook searching (SCS) algorithm is developed for fast VQ Encoding of images. This algorithm allows searching only a portion of the codebook to find the minimum distortion codeword of an input vector. In comparison with other existing fast VQ Encoding algorithms, this method requires the least number of multiplications as well as the least total number of operations. The requirement of extra memory at the encoder and pre-computation in the training stage of the SCS algorithm is also limited.
R. Seara - One of the best experts on this subject based on the ideXlab platform.
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Sorting Rates in Video Encoding Process for Complexity Reduction
IEEE Transactions on Circuits and Systems for Video Technology, 2010Co-Authors: M. Moecke, R. SearaAbstract:The motion estimation Process and coding mode selection are responsible for a large portion of the computational effort in H.264-based video Encoding systems optimized for rate-distortion (RD). This paper presents a rate sorting and truncation strategy that incorporates the RD optimization criterion in the decision to evaluate distortion for both motion vectors and coding modes. Experimental results confirm the effectiveness of the proposed approach, yielding up to a 90% reduction in the computational complexity. An additional saving can also be obtained, with insignificant RD performance loss, by using a quality threshold.
Liwei Guo - One of the best experts on this subject based on the ideXlab platform.
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content adaptive temporal search range control based on frame buffer utilization
Multimedia Signal Processing, 2006Co-Authors: Zhiqin Liang, Jiantao Zhou, Liwei GuoAbstract:Multiple reference frame selection adopted by the state-of-art H.264 video compression standard offers substantial performance gain. The temporal search range control, as a consequence, is crucial for maintaining the coding performance with minimum complexity. In this paper, we investigate the relationships between the reference frame buffer utilization and the optimal search range. A content-adaptive algorithm is proposed to control the search range dynamically during the Encoding Process. Experimental results show that our algorithm can rapidly adapt to the video characteristics and effectively reduce the complexity with negligible coding performance penalty.
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MMSP - Content-adaptive Temporal Search Range Control Based on Frame Buffer Utilization
2006 IEEE Workshop on Multimedia Signal Processing, 2006Co-Authors: Zhiqin Liang, Jiantao Zhou, Liwei GuoAbstract:Multiple reference frame selection adopted by the state-of-art H.264 video compression standard offers substantial performance gain. The temporal search range control, as a consequence, is crucial for maintaining the coding performance with minimum complexity. In this paper, we investigate the relationships between the reference frame buffer utilization and the optimal search range. A content-adaptive algorithm is proposed to control the search range dynamically during the Encoding Process. Experimental results show that our algorithm can rapidly adapt to the video characteristics and effectively reduce the complexity with negligible coding performance penalty.
Shao-yi Chien - One of the best experts on this subject based on the ideXlab platform.
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A Novel Gaming Video Encoding Process Using In-Game Motion Vectors
IEEE Transactions on Circuits and Systems for Video Technology, 2019Co-Authors: Sheng-de Wang, Shao-yi ChienAbstract:In this paper, we propose a motion preProcessing method for the use in the game application pipeline, which is composed of two stages: the proposed preProcessing method and the High Efficiency Video Coding (HEVC) encoder. The method accepts the object information from the game application, preProcesses the motion vectors of objects, and pass the preProcessed motion data to the HEVC encoder. The HEVC encoder takes the motion data as the initial (or the dedicated) value of motion estimation. Therefore, the traditional diamond search can be skipped and hence increase the Encoding performance of the HEVC encoder. In the motion preProcessing method, the following three steps are taken: a coordination system transformation, determining motion vectors for $4\times 4$ -checkerboard blocks [Atomic Block (AB)], and the selection of proper motion vectors for all varieties of prediction units in the encoder. With a focus on the special issues, such as move-out zone and bi-directional prediction, we are able to further optimize the performance of the encoder. We examined two types of 2D gaming scenes in our experiments. The experimental results show that, as compared with the original diamond search method provided by the encoder, our algorithm is able to achieve up to 49.0% time reduction of video Encoding. The Bjontegaard Delta bit rate can achieve up to −17.0% in the random_access mode while combining with the $\times 265$ encoder and up to −26.2% in the lowdelay mode while combining with the HM-16 encoder.
Kwok-tung Lo - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Encoding Algorithm for Vector Quantization of Images
Signal Processing, 1992Co-Authors: Kwok-tung Lo, Wai-kuen ChamAbstract:ABSTRACT: In vector quantization of images, the main problem is its computation complexity in the Encoding Process. In this work, a sub-codebook searching (SCS) algorithm is developed for fast VQ Encoding of images. This algorithm allows searching only a portion of the codebook to find the minimum distortion codeword of an input vector. In comparison with other existing fast VQ Encoding algorithms, this method requires the least number of multiplications as well as the least total number of operations. The requirement of extra memory at the encoder and pre-computation in the training stage of the SCS algorithm is also limited.