Kinds Of Video Sequences Research Articles

An Optimization Algorithm of Foreground Objects Extraction

In this paper, we propose a novel algorithm for coarse-to-fine foreground objects extraction. There are two general approaches for foreground objects extraction: background subtraction and image matting. Our new approach can not only improve detection accuracy compared with general background subtraction approaches, but also reduce computation burden compared with general image matting approaches. Firstly, we present a novel method called Motion-mask Gaussian Mixture Models (Motion-mask GMMs) to extract coarse foreground regions. This new approach can classify foreground and background pixels more accurately, especially when there are long-time stopping objects in the scene. Secondly, with the coarse foreground regions, we propose a novel approach to make foreground object extraction more accurate based on effective fusion of image registration and image matting. This new method overcomes the template drift problem during template updating and also reduces the expensive computational cost of image matting. Our proposed approach is tested with kinds of video sequences in indoor and outdoor environments. Experimental results demonstrate the accuracy and efficiency of our proposed approach for foreground object extraction.

An Adaptive Control for the Propagation Errors Incurred by DCT Coefficient-Dropping Transcoder

This paper presents a new distortion control scheme with a simple estimation model for the propagation errors incurred by dropping some parts of the bitstream in a frame dropping–coefficient dropping (FD-CD) transcoder. The primary goal of this paper is to facilitate bit-rate conversions and rate-distortion controls in the compressed domain without introducing a full decoding and re-encoding system in the pixel domain. First, the error propagation behavior over several frame sequences due to coefficient dropping is investigated on the basis of statistical and empirical properties. Then, such properties are used to develop a simple estimation model for the CD distortion accounting for the characteristics of the underlying coded-frame. Finally, the proposed estimation model allows us to determine the amount of coefficient dropping and to effectively allocate rate-distortions into coded-frames. Experimental results show that the proposed estimation model accurately describes the characteristics of propagation errors adaptively in the compressed domain and can be easily applied to distortion control over different kinds of video sequences.

Key Frame Extraction Using Unsupervised Clustering Based on a Statistical Model

This paper proposes a novel algorithm for extracting key frames to represent video shots. Regarding whether, or how well, a key frame represents a shot, different interpretations have been suggested. We develop our algorithm on the assumption that more important content may demand more attention and may last relatively more frames. Unsupervised clustering is used to divide the frames into clusters within a shot, and then a key frame is selected from each candidate cluster. To make the algorithm independent of video sequences, we employ a statistical model to calculate the clustering threshold. The proposed algorithm can capture the important yet salient content as the key frame. Its robustness and adaptability are validated by experiments with various kinds of video sequences.

Automatic text detection and removal in video sequences

This paper proposes an approach for automatic text detection and removal in video sequences based on support vector machines (SVMs) and spatiotemporal restoration. Given two consecutive frames, first, text regions in the current frame are detected by an SVM-based texture classifier. Second, two stages are performed for the restoration of the regions occluded by the detected text regions: temporal restoration in consecutive frames and spatial restoration in the current frame. Utilizing text motion and background difference, an input video sequence is classified and a different temporal restoration scheme is applied to the sequence. Such a combination of temporal restoration and spatial restoration shows great potential for automatic detection and removal of objects of interest in various kinds of video sequences, and is applicable to many applications such as translation of captions and replacement of indirect advertisements in videos.

Rate control algorithm for fast bit-rate conversion transcoding

In this paper, a new rate control algorithm is introduced as a solution for fast rate control requirements in transcoding. The specific transcoding problem mentioned in this paper is referred to as bit-rate conversion. For video services in heterogeneous network environments, it is necessary to convert the bit-rate of a compressed video to match it to transmission channels of lower capacity. The transcoding of coded video streams by requantization in the DCT (discrete cosine transform) domain is considered as a promising technique for its low complexity and acceptable picture quality. We carried out extensive simulations for many kinds of video sequences to find a novel R-Q (rate-quantization) model in the requantization process and found a piecewise linearly decreasing model. Exploiting this model we propose an efficient rate control algorithm for fast transcoding. Since the proposed algorithm requires much lower complexity than conventional algorithms and inherently avoids additional steps of implementation for a scene adaptive rate control in the transcoder, it has a significant advantage as a rate control algorithm for transcoding.