Showing papers on "Smacker video published in 2018"

On introducing timed tag-clouds in video lectures indexing

Abstract: The amount of digital material in video lecture archives is growing rapidly, causing the search&retrieval process to be time-consuming and almost impractical. Indeed, after the search, students receive a list of videos and often must use VCR-like functions to find the specific piece of video that covers the searched topic. Therefore, a more efficient method for video retrieval in digital video lecture archives is needed. In this paper, we propose VLB (Video Lecture Browsing), a system designed to facilitate both the retrieval of video lectures within video archives and the finding of the most appropriate segment of a video lecture that covers a searched topic by automatically producing a general picture of the contents of a video lecture. To achieve these goals, the system introduces the idea of timed tag-clouds, which are produced with a combination of aural and visual analysis. Results of a MOS evaluation show that users highly appreciate the timed tag-clouds approach and a comparison study against other popular approaches shows that 93 % of users prefer to use VLB to handle video lectures.

A novel compact yet rich key frame creation method for compressed video summarization

Abstract: Video summarization has great potential to enable rapid browsing and efficient video indexing in many applications. In this study, we propose a novel compact yet rich key frame creation method for compressed video summarization. First, we directly extract DC coefficients of I frame from a compressed video stream, and DC-based mutual information is computed to segment the long video into shots. Then, we select shots with static background and moving object according to the intensity and range of motion vector in the video stream. Detecting moving object outliers in each selected shot, the optimal object set is then selected by importance ranking and solving an optimum programming problem. Finally, we conduct an improved KNN matting approach on the optimal object outliers to automatically and seamlessly splice these outliers to the final key frame as video summarization. Previous video summarization methods typically select one or more frames from the original video as the video summarization. However, these existing key frame representation approaches for video summarization eliminate the time axis and lose the dynamic aspect of the video scene. The proposed video summarization preserves both compactness and considerably richer information than previous video summaries. Experimental results indicate that the proposed key frame representation not only includes abundant semantics but also is natural, which satisfies user preferences.

Region-Based Multiple Description Coding for Multiview Video Plus Depth Video

Abstract: Interframe and interview predictions are widely employed in multiview video coding. This technique improves the coding efficiency, but it also increases the vulnerability of the coded bitstream. Thus, one packet loss will affect many subsequent frames in the same view and probably in other referenced views. To address this problem, a region-based multiple description coding scheme is proposed for robust 3-D video communication in this paper, in which two descriptions are formed by setting the left and right view as dominant in the first and second description, respectively. This approach exploits the fact that most regions in the reference view could be synthesized from the base view. Hence, these regions could be skipped or only coarsely encoded. In our work, the disoccluded regions, illumination-affected regions, and remaining regions are first determined and extracted. By assigning different quantization parameters for these three different regions according to the network status, an efficient multiple description scheme is formed. Experimental results demonstrate that the proposed scheme achieves considerably better performance compared with the traditional approach.

Key frame extraction scheme based on sliding window and features

Abstract: With the rapid development of the Internet and P2P technology, multimedia resources are gradually adding and used widely Since network traffic increases sharply, how to choose the interested information for a number of Internet users is challenging So, technologies and applications, such as video search, video fast browsing, video index and storage are in great demand Behind these technologies and applications, an important problem is how to quickly browse massive video data and obtain the main content of the video To solve this problem, different key frame extraction algorithms have been proposed Due to the diversity of video content, different video have different characteristics So the design of general video key frame extraction algorithm to solve the problem is not the reality The main trend for the problem is to design the key frame extraction algorithm based on the characteristics of the video itself In this article, we mainly focus on videos with edited boundaries and shot conversions Aiming at this kind of video, we have designed and implemented video key frame extraction algorithm based on sliding window, the global feature Gist and local feature point detection algorithm SURF In this algorithm, we use Gist feature to construct the global scene information of frames,and the SURF key point detection algorithm to extract local key points as local feature for each frame Then, shot segmentation based on sliding window and shot merging algorithm is applied to dividing the original video into several shots After that,we select the most representative frames in each video shot as key frames Finally we evaluate the result of the algorithm from the subjective and objective perspective Results show that key frames extracted in the algorithm are of high quality and can basically cover the main content of the original video

Three-dimensional video encoding method, three-dimensional video decoding method, and related apparatus

Abstract: A three-dimensional (3D) video encoding method, decoding method, and related apparatus is disclosed. The decoding method may include decoding a video bitstream to obtain a single sample flag bit corresponding to a current image block in a current depth map, performing detection on a first adjacent prediction sampling point and a second adjacent prediction sampling point of the current image block in the current depth map if the single sample flag bit obtained by decoding indicates that a decoding mode corresponding to the current image block is a single depth intra-frame mode (SDM), and constructing a sample candidate set according to results of the detection on the first adjacent prediction sampling point and the second adjacent prediction sampling point, where the sample candidate set includes a first index location and a second index location, decoding the video bitstream to obtain a single sample index flag bit corresponding to the current image block.

Power-Efficient Video System Design

Abstract: This chapter provides an overview of designing a video system to meet the challenges outlined in Chap. 2. Details are given about the architectural aspects, and the complexity and power control knobs of the system. By examining these knobs, motivational analysis is carried out which forms the foundation of the algorithmic- and architectural-design decisions presented in this book.

Optimization of Interactive Live Free Viewpoint Multiview Video Streaming Bandwidth

Abstract: Free Viewpoint Multiview Video TV (FTV) allows a user to pan horizontally within a video field of view. Interactive live FTV allows a user to experience being live at a soccer game with the interactive capability to freely look from side-to-side. Current efforts to commercialize interactive live FTV have failed due to a lack of standardization and unacceptably high real-world DVB (Direct Video Broadcast) system bandwidth requirements. In this paper, we provide a solution called OLFVmv (Optimized Live Free Viewpoint Multiview Video) that applies adaptive prediction of future video content requirements based on past viewer behavior, thereby dramatically reducing system bandwidth requirements. To expand, OLFVmv entails algorithms and a system architecture that allows for the intelligent segmentation and prioritization of video content based on video compression layering combined with viewer behavior statistics. Based on our positive results, we show that system operators can practically implement OLFVmv into existing systems.