WeChat Mini Program
Old Version Features

    • A Fast Algorithm of CU Block Division Based on Frequency Domain on VVC

    Zihao Liu,Lei Chen,Chenggang Xu

    Signal and Information Processing, Networking and Computers（2023）

    Beijing University of Posts and Telecommunications

    Cited 0|Views6
    Abstract
    As the most mainstream codec standard in the world, VVC has been widely used all over the world and widely serves our lives. Compared with the previous generation of coding standards, in order to meet the needs of modern media, VVC has more feature that improves the coding quality, but also brings higher time complexity. In order to be better used in engineering, VVC coding needs to introduce a fast algorithm to reduce the time complexity. This paper proposes a method based on the fast algorithm of CU block division in the frequency domain assists the selection of the fast division mode by analyzing the texture features of the image to reduce the time complexity. At the same time, on this basis, optimization is also made to further reduce the time. It can be confirmed by the results that the algorithm can indeed reduce the time complexity with the least performance loss, and it is an effective algorithm.
    More
    Translated text
    Key words
    cu block division,fast algorithm,frequency domain
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined