Literature DB >> 20693113

Direct intermode selection for H.264 video coding using phase correlation.

Manoranjan Paul1, Weisi Lin, Chiew Tong Lau, Bu-Sung Lee.   

Abstract

The H.264 video coding standard exhibits higher performance compared to the other existing standards such as H.263, MPEG-X. This improved performance is achieved mainly due to the multiple-mode motion estimation and compensation. Recent research tried to reduce the computational time using the predictive motion estimation, early zero motion vector detection, fast motion estimation, and fast mode decision, etc. These approaches reduce the computational time substantially, at the expense of degrading image quality and/or increase bitrates to a certain extent. In this paper, we use phase correlation to capture the motion information between the current and reference blocks and then devise an algorithm for direct motion estimation mode prediction, without excessive motion estimation. A bigger amount of computational time is reduced by the direct mode decision and exploitation of available motion vector information from phase correlation. The experimental results show that the proposed scheme outperforms the existing relevant fast algorithms, in terms of both operating efficiency and video coding quality. To be more specific, 82 ~92% of encoding time is saved compared to the exhaustive mode selection (against 58 ~74% in the relevant state-of-the-art), and this is achieved without jeopardizing image quality (in fact, there is some improvement over the exhaustive mode selection at mid to high bit rates) and for a wide range of videos and bitrates (another advantages over the relevant state-of-the-art).

Year:  2010        PMID: 20693113     DOI: 10.1109/TIP.2010.2063436

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  2 in total

1.  Fast Mode Decision in the HEVC Video Coding Standard by Exploiting Region with Dominated Motion and Saliency Features.

Authors:  Pallab Kanti Podder; Manoranjan Paul; Manzur Murshed
Journal:  PLoS One       Date:  2016-03-10       Impact factor: 3.240

Review 2.  Machine Learning-Based Epileptic Seizure Detection Methods Using Wavelet and EMD-Based Decomposition Techniques: A Review.

Authors:  Rabindra Gandhi Thangarajoo; Mamun Bin Ibne Reaz; Geetika Srivastava; Fahmida Haque; Sawal Hamid Md Ali; Ahmad Ashrif A Bakar; Mohammad Arif Sobhan Bhuiyan
Journal:  Sensors (Basel)       Date:  2021-12-20       Impact factor: 3.576

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.