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 :
بازگشت