Automatic region-of-interest detection and prioritisation for visually optimised coding of low bit rate videos
Himawan, Ivan, Song, Wei, & Tjondronegoro, Dian W. (2013) Automatic region-of-interest detection and prioritisation for visually optimised coding of low bit rate videos. In Proceedings of the 2013 IEEE Workshop on Applications of Computer Vision (WACV), IEEE, Clearwater Beach, Florida, USA, pp. 76-82.
|Updated Version (PDF 650Kb) |
Administrators only | Request a copy from author
The increasing popularity of video consumption from mobile devices requires an effective video coding strategy. To overcome diverse communication networks, video services often need to maintain sustainable quality when the available bandwidth is limited. One of the strategy for a visually-optimised video adaptation is by implementing a region-of-interest (ROI) based scalability, whereby important regions can be encoded at a higher quality while maintaining sufficient quality for the rest of the frame.
The result is an improved perceived quality at the same bit rate as normal encoding, which is particularly obvious at the range of lower bit rate. However, because of the difficulties of predicting region-of-interest (ROI) accurately, there is a limited research and development of ROI-based video coding for general videos. In this paper, the phase spectrum quaternion of Fourier Transform (PQFT) method is adopted to determine the ROI. To improve the results of ROI detection, the saliency map from the PQFT is augmented with maps created from high level knowledge of factors that are known to attract human attention. Hence, maps that locate faces and emphasise the centre of the screen are used in combination with the saliency map to determine the ROI.
The contribution of this paper lies on the automatic ROI detection technique for coding a low bit rate videos which include the ROI prioritisation technique to give different level of encoding qualities for multiple ROIs, and the evaluation of the proposed automatic ROI detection that is shown to have a close performance to human ROI, based on the eye fixation data.
Citation countsare sourced monthly fromand citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Conference Paper|
|Keywords:||Video Coding, Region of Interest Detection|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTER SOFTWARE (080300) > Multimedia Programming (080305)|
|Divisions:||Current > Schools > School of Information Systems|
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||24 Oct 2012 09:05|
|Last Modified:||25 Apr 2013 04:17|
Repository Staff Only: item control page