Motion Vectors Of Macroblocks Research Articles

Human Visual System-based Unequal Error Protection for Robust Video Coding

To increase the overall visual quality of the video services without increasing data rate, a human visual system-based video coding, founded on a hierarchy of the video stream in different levels of importance, is developed. Determining these importance levels takes in count three classification criteria: the position of current image in the group of images (image level), the importance of the motion vectors of macroblocks in the current image (macroblock level) and belonging or not of a pixel in a spatial region of interest (pixel level). At the end of this classification process, an interpolation between the results of the three-level selection allows to establish an index of importance for each macroblock of the image to be encoded. This index determines the type of channel coding to be applied to the corresponding macroblock. Tests have shown that the technique presented in this paper achieves better results in PSNR and SSIM (structural similarity) than an equal error protection technique.

A Practical Video Fragment Identification System

In this paper, we present a practical identification approach of video fragment for digital video files. Before analyzing the video content, we must decode it based on its encoding format first. In order to effectively identify the format of a fragment, a format classification is performed before the format identification. The methods of format classification and identification are discriminative subspace clustering (DiSC) and the Knearest neighbor (KNN).Because of losing the meta-information, we add a maximum similar header (MSH) to the front of the fragment to recover the video content. We adopt a simple key frame detection method using standard deviation and mean value. Motion vectors of macro blocks are utilized to classify the video features for effectively identifying the video. Several edges of frames are accumulated and compose a video feature. The experimental results show the evaluations of the video format classification and identification, fragment recovery, and content identification.

Monte-Carlo-Based Parametric Motion Estimation Using a Hybrid Model Approach

Parametric motion estimation is an important task for various video processing applications, such as analysis, segmentation, and coding. The process for such an estimation has to satisfy three requirements. It has to be fast, accurate, and robust in the presence of arbitrarily moving foreground objects. We introduce a two-step simplification scheme, suitable for Monte-Carlo-based perspective motion model estimation. For complexity reduction, the Helmholtz tradeoff estimator as well as random sample consensus are enhanced with this scheme and applied on Kanade–Lucas–Tomasi features as well as on video stream macroblock motion vector fields. For the feature-based estimation, good trackable features are detected and tracked on raw video sequences. For the block-based approach, motion vector fields from encoded H.264/AVC video streams are used. Results indicate that the complexity of the whole estimation process can be reduced by a factor of up to 10000 compared to state-of-the-art methods without losing estimation precision.

Comprehensive scheme for subpixel variable block-size motion estimation

Fast variable block-size motion estimation is a key issue for real-time applications of the H.264, whereas the subpixel refinement takes up much computational time as compared to integer-pixel mo- tion estimation. We propose a new fast subpixel precision variable block-size motion-estimation scheme. This algorithm uses the sta- tistical information, which comes from the motion activities of the macroblocks (MBs) in the previous frame, to predict the character- istics of MBs in the current frame. Additionally, the distortion values and motion vectors of MBs in the previous frame are also considered as prior knowledge, based on which we can make decisions on early mode selection and early termination, and on whether or not to skip some candidate modes and candidate checking points. The interme- diate results of subpixel motion estimation are used together with the prior knowledge to reduce subpixel search time when searching for stationary blocks. Our new directional information strategy is used in both integer-pixel motion estimation and subpixel motion estima- tion to accelerate the search procedure. Moreover, our algorithm can eliminate the subpixel motion estimation of all the unselected sub- partition modes. The computational resources can then be spent on the modes and locations that deserve to be searched more than oth- ers. Extensive experimental is been done, the results of which show that the speed of our approach is nearly five times that of the fast algorithms in H.264 JM, with a better peak signal-to-noise ratio and better bit performance. © 2011 SPIE and IS&T. (DOI: 10.1117/1.3553438)