4/27/2023 0 Comments Inmotion compress optimize imagesHowever, the algorithm is prone to artifacts in the process of video image compression, resulting in poor quality of compressed video image. Experiments show that the architecture can reduce the coding cycle by at least 52.3% at the cost of increasing the number of circuit gates by about 1568. According to the characteristics that there is no data correlation in wedge block evaluation, a hardware circuit of DMM-1 encoder with 5-stage pipeline architecture is proposed in order to reduce the coding cycle required for a depth block coding. In order to further improve the performance of DMM-1 encoder in 3D-HEVC, this paper studies the DMM-1 algorithm architecture. When designing the DMM-1 encoder circuit, the coding cycle of the traditional architecture circuit is long it can only meet the requirements of real-time video coding with low resolution and frame rate. The new mode not only improves the coding quality, but also improves the complexity of the original algorithm. In, in order to better encode the depth map in 3D video, this paper introduces the depth modeling mode (DMMs) into the 3D-HEVC standard. However, the proposed algorithm does not compensate the frame rate of the video image, resulting in low definition of the compressed video image. At the same time, compared with other algorithms, under the same bit rate, the average PSNR value of the reconstructed video can be increased by 0.5 dB. The experimental results show that compared with HEVC standard, the proposed algorithm can save a certain bit rate under the same video quality. Finally, the frame rate up-conversion technology based on motion compensation is used to restore the video sequence to the original frame rate. Then, combined with the motion vector extracted from the H EVC code stream at the decoding end, the forward and backward joint motion estimation is used to further refine it, so that the refined motion vector is closer to the real motion of the object. Secondly, the low frame rate video is encoded and decoded by HEVC. Firstly, the original video is extracted at the encoding end to reduce the video frame rate. Aiming at the problem that the effect of frame rate up conversion directly using the motion vector of low frame rate video in HEVC bitstream information is not ideal, a video compression algorithm based on the combination of frame rate up conversion and HEVC based on motion vector refinement is proposed in this paper. In, the combination of frame rate conversion technology and new video compression coding standard HEVC is conducive to improve the video compression efficiency. Therefore, it is necessary to compress the low frame rate video animation video. In the power grid operation site, it is necessary to transmit the video and image of the field operation in the channel with limited bandwidth at low bit rate, but the transmission bandwidth and storage capacity of the video image are difficult to meet the requirements when the definition and resolution of the video image are continuously enhanced. With the rapid development of aerospace imaging technology, portable devices, and mobile terminals, low bit rate images are more and more widely used. With the development of Internet technology, image storage technology and image transmission technology have become the core content of multimedia. The experimental results show that the low frame rate video and animation video compressed by the proposed algorithm have high definition, high compression quality under different compression ratios, and high compression efficiency under different compression ratios. After the frame rate compensation, the low frame rate video animation video is divided into blocks, the CS value of the image block is measured, the linear estimation of the image block is carried out by using the linear function model, and the compression of the low frame rate video animation video is completed according to the best linear estimation result. According to the estimation results, the video frames are compensated to eliminate the artifacts of low frame rate video animation. In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video animation using local convolution. In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm. When using the current method to compress the low frame rate video animation video, there is no frame rate compensation for the video image, which cannot eliminate the artifacts generated in the compression process, resulting in low definition, poor quality, and low compression efficiency of the compressed low frame rate video animation video.
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