Parallelization of intra prediction algorithm based on array processor

For the characteristics of intra prediction algorithms, the data dependence and parallelism between intra prediction models are first analyzed. This paper proposes a parallelization method based on dynamic reconfigurable array processors provided by the project team, and uses data level parallel(DLP) algorithms in multi-core units. The experimental results show that Y-component of peak signal to noise ratio(Y-PSNR) is improved about 10 dB and the time is saved 63% compared with high-efficiency video coding(HEVC) test model HM10.0. This method can effectively reduce codec time of the video and reduce computational complexity.

Defining Embedding Distortion for Intra Prediction Mode-Based Video Steganography

In this paper,an effective intra prediction mode-based video strganography is proposed.Secret messages are embedded during the intra prediction of the video encoding without causing large embedding impact.The influence on the sum of absolute difference(SAD)in intra prediction modes(IPMs)reversion phenomenon is sharp when modifying IPMs.It inspires us to take the SAD prediction deviation(SPD)to define the distortion function.What is more,the mapping rule between IPMs and the codewords is introduced to further reduce the SPD values of each intra block.Syndrome-trellis code(STC)is used as the practical embedding implementation.Experimental results demonstrate that our proposed steganographic scheme presents high undetectability compared with existing IPMs-based steganographic approaches.It also outperforms these schemes on stego video quality.

Edge Feature Based Fast Mode Decision Algorithm for H.264 Intra Prediction

A fast intra mode decision algorithm is proposed in this paper to reduce the complexity of H. 264 encoder. The proposed algorithm adopted the pre-processing method based on edge feature in pictures to filter out some impossible prediction modes. Context information and pre-computed threshold are used to determine whether it is necessary to check the DC mode. This method is able to get rid of most of candidate modes so that only 66--150 modes are left for the final mode decision, instead of 592 modes in the case of full search （FS） method of H. 264. Simulation results demonstrate that the coding time of the proposed algorithm falls down 71.7% compared with FS method, while the performance loss is trivial compared with FS mode decision scheme.

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications,there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality.Versatile Video Coding(VVC)is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding(HEVC).In order to achieve this improved coding performance,VVC adopted various advanced coding tools,such as flexible Multi-type Tree(MTT)block structure which uses Binary Tree(BT)split and Ternary Tree(TT)split.However,VVC encoder requires heavy computational complexity due to the excessive Ratedistortion Optimization(RDO)processes used to determine the optimalMTT block mode.In this paper,we propose a fast MTT decision method with two Lightweight Neural Networks(LNNs)using Multi-layer Perceptron(MLP),which are applied to determine the early termination of the TT split within the encoding process.Experimental results show that the proposed method significantly reduced the encoding complexity up to 26%with unnoticeable coding loss compared to the VVC TestModel(VTM).

Dominant edge direction based fast intra mode decision in the H.264/AVC encoder

The H.264/AVC video coding standard uses an intra prediction mode with 4×4 and 16×16 blocks for luma and 8×8 blocks for chroma. This standard uses the rate distortion optimization (RDO) method to determine the best coding mode based on the compression performance and video quality. This method offers a large improvement in coding efficiency compared to other compression standards, but the computational complexity is greater due to the various intra prediction modes. This paper proposes a fast intra mode decision algorithm for real-time encoding of H.264/AVC based on the dominant edge direction (DED). The DED is extracted using pixel value summation and subtraction in the horizontal and vertical directions. By using the DED, three modes instead of nine are chosen for RDO calculation to decide on the best mode in the 4×4 luma block. For the 16×16 luma and the 8×8 chroma, only two modes are chosen instead of four. Experimental results show that the entire encoding time saving of the proposed algorithm is about 67% compared to the full intra search method with negligible loss of quality.

Low-Complexity Tools in AVS Part 7

Audio Video coding Standard （AVS） is established by the AVS Working Group of China. The main goal of AVS part 7 is to provide high compression performance with relatively low complexity for mobility applications. There are 3 main low-complexity tools： deblocking filter, context-based adaptive 2D-VLC and direct intra prediction. These tools are presented and analyzed respectively. Finally, we compare the performance and the decoding speed of AVS part 7 and H.264 baseline profile. The analysis and results indicate that AVS part 7 achieves similar performance with lower cost.

A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence

In this paper,a novel compression framework based on 3D point cloud data is proposed for telepresence,which consists of two parts.One is implemented to remove the spatial redundancy,i.e.,a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box.The other part is applied to remove the temporal redundancy of the 3D point cloud data.The temporal redundancy between point clouds is removed by using the motion vector,i.e.,the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame.The hrst,the B-SHOT(binary signatures of histograms orientation)descriptor is applied to represent the point feature for matching the corresponding point between two frames.The second,the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame.The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames.Finally,the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the curren t and the motion vectors are transmit ted into the rem ote end.In order to reduce calculation time of the B-SHOT descriptor,we introduce an octree structure into the B-SHOT descriptor.In particular,in order to improve the robustness of the matching operation,we design the cluster feature to estimate the similarity bet ween two clusters.Experimen tai results have shown the bet ter performance of the proposed method due to the lower calculation time and the higher compression ratio.The proposed met hod achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.