A regression method for real-time video quality evaluation

No-Reference (NR) metrics provide a mechanism to assess video quality in an ever-growing wireless network. Their low computational complexity and functional characteristics make them the primary choice when it comes to realtime content management and mobile streaming control. Unfortunately, common NR metrics suer from poor accuracy, particularly in network-impaired video streams. In this work, we introduce a regression-based video quality metric that is simple enough for real-time computation on thin clients, and comparably as accurate as state-of-the-art Full-Reference (FR) metrics, which are functionally and computationally inviable in real-time streaming. We benchmark our metric against the FR metric VQM (Video Quality Metric), finding a very strong correlation factor

Heterogeneous video transcoding to lower spatio-temporal resolutions and different encoding formats

different encoding format

Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition

This work introduces two novel approaches for feature extraction applied to video-based Arabic sign language recognition, namely, motion representation through motion estimation and motion representation through motion residuals. In the former, motion estimation is used to compute the motion vectors of a video-based deaf sign or gesture. In the preprocessing stage for feature extraction, the horizontal and vertical components of such vectors are rearranged into intensity images and transformed into the frequency domain. In the second approach, motion is represented through motion residuals. The residuals are then thresholded and transformed into the frequency domain. Since in both approaches the temporal dimension of the video-based gesture needs to be preserved, hidden Markov models are used for classification tasks. Additionally, this paper proposes to project the motion information in the time domain through either telescopic motion vector composition or polar accumulated differences of motion residuals. The feature vectors are then extracted from the projected motion information. After that, model parameters can be evaluated by using simple classifiers such as Fisher's linear discriminant. The paper reports on the classification accuracy of the proposed solutions. Comparisons with existing work reveal that up to 39% of the misclassifications have been corrected

Loss concealment using B-pictures motion information

In this work, the motion parameters of the bi-directionally predicted pictures (B-pictures) of MPEG-1,2 are exploited for concealment of large portions of corrupted anchor pictures that might arise due to channel errors or packet losses. To further enhance the quality of the concealed anchor pictures, we propose two methods of constraining the motion vectors of the B-pictures that strengthen the tie between them and those of the anchor pictures in the same picture sub-group. In one method, the macroblock decisions on the last B-picture in each sub-group is constrained to be bi-directional if those of the other B-pictures are not, such that the derived motion vectors for the concealment of the anchor picture are always composed from the forward and backward motion vectors of the bi-directional motions. Second, the bi-directional motion vectors of the B-pictures in each sub-group is constrained such that the vectorial sum of their forward and backward motion vectors results in accurate motion prediction of the anchor picture. The experimental results show that while the composed motion vectors improve the quality of concealment over the conventional methods by more than 3-4 dB, another 2 dB improvement can be achieved by constraining the generation of the bi-directional motion vectors. * Author for correspondence