Low complexity video compression using moving edge detection based on DCT coefficients

In this paper, we propose a new low complexity video compression method based on detecting blocks containing moving edges us- ing only DCT coe±cients. The detection, whilst being very e±cient, also allows e±cient motion estimation by constraining the search process to moving macro-blocks only. The encoders PSNR is degraded by 2dB com- pared to H.264/AVC inter for such scenarios, whilst requiring only 5% of the execution time. The computational complexity of our approach is comparable to that of the DISCOVER codec which is the state of the art low complexity distributed video coding. The proposed method ¯nds blocks with moving edge blocks and processes only selected blocks. The approach is particularly suited to surveillance type scenarios with a static camera

Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Study on Segmentation and Global Motion Estimation in Object Tracking Based on Compressed Domain

Object tracking is an interesting and needed procedure for many real time applications. But it is a challenging one, because of the presence of challenging sequences with abrupt motion occlusion, cluttered background and also the camera shake. In many video processing systems, the presence of moving objects limits the accuracy of Global Motion Estimation (GME). On the other hand, the inaccuracy of global motion parameter estimates affects the performance of motion segmentation. In the proposed method, we introduce a procedure for simultaneous object segmentation and GME from block-based motion vector (MV) field, motion vector is refined firstly by spatial and temporal correlation of motion and initial segmentation is produced by using the motion vector difference after global motion estimation

Efficient Video Transport over Lossy Networks

Nowadays, packet video is an important application of the Internet. Unfortunately the capacity of the Internet is still very heterogeneous because it connects high bandwidth ATM networks as well as low bandwidth ISDN dial in lines. The MPEG-2 and MPEG-4 video compression standards provide efficient video encoding for high and low bandwidth media streams. In particular they include two paradigms which make those standards suitable for the transmission of video via heterogeneous networks. Both support layered video streams and MPEG-4 additionally allows the independent coding of video objects. In this paper we discuss those two paradigms, give an overview of the MPEG video compression standards and describe transport protocols for Real Time Media transport over lossy networks. Furthermore, we propose a real-time segmentation approach for extracting video objects in teleteaching scenarios