Computer vision aided video coding

1.1 Abstract Despite the natural synergy among image understanding and image processing research on computer vision (e.g., video surveillance) and visual information compression (e.g., image/video coding) has been carried out mostly in isolation. For high performance we need to integrate the knowledge from computer vision and video coding technology. This need arises due to the recent expansion of new technologies based on the wireless networks in the burgeoning Internet, close-circuits security cameras, mobile devices, home entertainment, and YouTube. While wired networks today enjoy almost unbounded bandwidth, wireless networks still operate under stringent bandwidth constrains. Thus, video communication over wireless networks requires improvement in the areas of compression, quality, and computational complexity in the video coding technology. In this chapter, we will cover these areas based on recent research. Firstly we present dynamic background modeling techniques to generate a dynamic frame popularly known as McFIS (the most common frame of a scene) from dynamically challenging environments for 2 detecting moving objects, secondly we present a number of advanced video coding techniques for improving rate-distortion performance as well as reducing computational complexity compared to the state-of-the-arts methods, and finally we present computer vision aided video coding technique by combining computer vision technique (such as dynamic background) and video compression technique to improve the coding performance in term of quality (subjective and objective), compression, and computational complexity for stringent bandwidth carrier such as wireless network.

[1]  IEEE conference on computer vision and pattern recognition , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[2]  Xiaoyang Wu,et al.  L-shaped segmentations in motion-compensated prediction of H.264 , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[3]  Dmitry B. Goldgof,et al.  Ieee Transactions on Image Processing Fusion of Physically-based Registration and Deformation Modeling for Nonrigid Motion Analysis 1 , 2007 .

[4]  Mike E. Davies,et al.  IEEE International Conference on Acoustics Speech and Signal Processing , 2008 .

[5]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Bu-Sung Lee,et al.  Direct Intermode Selection for H.264 Video Coding Using Phase Correlation , 2011, IEEE Transactions on Image Processing.

[7]  Xin Li,et al.  Geometry-Adaptive Block Partitioning for Video Coding , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[8]  Antonio Ortega,et al.  Motion compensation based on implicit block segmentation , 2008, 2008 15th IEEE International Conference on Image Processing.

[9]  Pascal Frossard,et al.  IEEE Transactions on Circuits and Systems for Video Technology , 2008 .

[10]  Elsevier Sdol,et al.  Journal of Visual Communication and Image Representation , 2009 .

[11]  O. Patrouix,et al.  Dynamic Background Segmentation for Remote Reference Image Updating within Motion Detection JPEG2000 , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[12]  Liang-Gee Chen,et al.  Analysis and complexity reduction of multiple reference frames motion estimation in H.264/AVC , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Manoranjan Paul,et al.  Efficient Video Coding Considering a Video as a 3D Data Cube , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.

[14]  No Value,et al.  IEEE International Conference on Image Processing , 2003 .

[15]  Itu-T and Iso Iec Jtc.  Advanced video coding for generic audiovisual services , 2010 .

[16]  Choudhury A. Rahman,et al.  UMHexagonS algorithm based motion estimation architecture for H.264/AVC , 2005, Fifth International Workshop on System-on-Chip for Real-Time Applications (IWSOC'05).

[17]  Qionghai Dai,et al.  Background-frame based motion compensation for video compression , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[18]  Neill W Campbell,et al.  IEEE International Conference on Computer Vision and Pattern Recognition , 2008 .

[19]  Zhengdao Wang.  IEEE TRANSACTIONS ON COMMUNICATIONS (SUBMITTED) All-Digital Uni cation and Equalization of Generalized Multi-Carrier CDMA through Frequency-Selective Uplink Channelsy , 2000 .

[20]  M.N.S. Swamy,et al.  IEEE TRANSACTIONS ON MULTIMEDIA STEERING COMMITTEE MEMBERS , 2005 .