DocumentCode
1713714
Title
H.264/AVC Intra-only Coding (iAVC) and Neural Network Based Prediction Mode Decision
Author
Yang, Ming ; Bourbakis, Nikolaos
Author_Institution
Dept. of Comput. Sci., Montclair State Univ., Montclair, NJ, USA
Volume
2
fYear
2010
Firstpage
57
Lastpage
60
Abstract
The requirement to transmit video data over unreliable wireless networks is anticipated in the foreseeable future. Significant compression ratio and error resilience are both needed for applications including tele-operated robotics, vehicle-mounted cameras, sensor network, etc. Block-matching based inter-frame coding techniques, such as MPEG-x and H.26x, do not perform well in these scenarios due to error propagation between frames. Intra-only coding technologies, such as Motion-JPEG, exhibit better recovery from network data loss at the price of higher data rates. In order to address these issues, an intra-only coding scheme of H.264/AVC (iAVC) is proposed. In this approach, each frame is coded independently as an I-frame. In order to speed up the coding procedure, we propose a neural network based intra-only prediction mode decision approach, which has the potential to significantly reduce coding complexity. Frame copy is applied to compensate for packet loss. The proposed approach is a good balance between compression performance, memory usage, and error resilience. It achieves compression performance comparable to Motion-JPEG2000, with lower complexity. Low computational complexity and memory usage are very crucial to mobile stations and devices in wireless networks.
Keywords
computational complexity; data compression; error analysis; image matching; neural nets; video coding; H.264 AVC intra-only coding; MPEG-x; block matching based inter frame coding techniques; compression ratio; computational coding complexity reduction; error propagation; error resilience; motion-JPEG2000; neural network based intra only prediction mode decision approach; neural network based prediction mode decision; video data transmission; wireless networks; Artificial neural networks; Automatic voltage control; Complexity theory; Encoding; Image coding; Resilience; Streaming media; H.264/AVC; Motion-JPEG2000; Video; coding; errorresilience; network; wireless;
fLanguage
English
Publisher
ieee
Conference_Titel
Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on
Conference_Location
Arras
ISSN
1082-3409
Print_ISBN
978-1-4244-8817-9
Type
conf
DOI
10.1109/ICTAI.2010.84
Filename
5671429
Link To Document