Scene-effect detection and insertion MPEG encoding scheme for video browsing and error concealment

For an MPEG coding scheme, the encoder can do more than video compression. In this paper, a novel MPEG codec embedded with scene-effect information detection and insertion is proposed to provide more functionality at the decoder end. Based on the macroblock (MB) type of information that is generated simultaneously in the encoding process, a single-pass automatic scene-effect insertion MPEG coding scheme can be achieved. Using the USER/spl I.bar/DATA of picture header, the video output bitstreams by our method still conform to the conventional MPEG decoding system. The proposed method provides a solution toward upgrading the existing MPEG codec with low complexity to accomplish at least two major advantages. Precise and effective video browsing resulting from the scene-effect extraction can significantly reduce the user's time to look up what they are interested in. For video transmission, the bitstreams containing scene-effect information can obtain better error concealment performance when scene changes are involved. Compared with the gain it achieves, the payout of our algorithm is very worthy with comparatively small efforts.     

Effective wipe detection in MPEG compressed video using macro block type information

For video scene analysis, the wipe transition is considered most complex and difficult to detect. In this paper, an effective wipe detection method is proposed using the macroblock (MB) information of the MPEG compressed video. By analyzing the prediction directions of B frames, which are revealed in the MB types, the scene change region of each frame can be found. Once the accumulation of the scene change regions covers most of the area of the frame, the sequence will be considered a motionless wipe transition sequence. Besides, uncommon intracoded MB of the B frame can also be applied as an indicator of the motion wipe transition. A very simple analysis based on small amount of MB type information is sufficient to achieve wipe detection directly on MPEG compressed video. Easy extraction of MB type information, low-complexity analysis algorithm and robustness to arbitrary shape and direction of wipe transitions are the great advantages of the proposed method.     

一种快速的压缩域视频流场景分段算法

提出了一种快速的压缩域视频流场景分段算法，对视频流进行两次分析：第一次分析（粗略分析）只分析P-帧中宏块统计信息，检测出可能存在的镜头边界；第二次分析（精确分析）再对粗略分析找出的边界邻近的B-帧和P-帧的宏块类型进行分析。从而对场景变换进行精确分析和定位，实验结果表明，粗略分析可以满足实时检测的速度要求，帧定位误差控制在10帧之内，精确分析可以进一步把帧定位误差控制在2帧之内。     

Scene similarity measure for video content segmentation in the framework of a rough indexing paradigm

This article presents a scene similarity measure for video content segmentation. In the context of the rough indexing paradigm, we extract only partial information from MPEG compressed streams to measure the similarity of video frames through time. The similarity measure of I‐Frames is defined based on motion compensation of DC images and local contrast computation. The method allows a real‐time segmentation of the video content. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 765–783, 2006.     

一种新的基于运动矢量的MPEG VIDEO码流cut检出算法

介绍了一种对ＭＰＥＧ ＶＩＤＥＯ码流中场景切换点进行高速检出的新方法,在计算各帧相对于预测帧的类似度之后进行了类似度的归一化,降低了ｃｕｔ检出算法的复杂度,提高了检出的速度。     

Automatic scene detection for advanced story retrieval

Browsing video scenes is just the process to unfold the story scenarios of a long video archive, which can help users to locate their desired video segments quickly and efficiently. Automatic scene detection of a long video stream file is hence the first and crucial step toward a concise and comprehensive content-based representation for indexing, browsing and retrieval purposes. In this paper, we present a novel scene detection scheme for various video types. We first detect video shot using a coarse-to-fine algorithm. The key frames without useful information are detected and removed using template matching. Spatio-temporal coherent shots are then grouped into the same scene based on the temporal constraint of video content and visual similarity of shot activity. The proposed algorithm has been performed on various types of videos containing movie and TV program. Promising experimental results shows that the proposed method makes sense to efficient retrieval of video contents of interest.     

Novel automatic video cut detection technique using Gabor filtering

Video shot transition identification constitutes an important computer vision research field, being applied, as an essential step, in many other digital video analysis domains: video scene detection, video compression, video indexing, video content retrieval and video object tracking. This paper approaches the video cut transition detection domain, providing a novel feature-based automatic identification method. We propose a feature extraction technique that uses 2D Gabor filtering, computing tridimensional image feature vectors for the video frames. Most shot cut detection techniques use a thresholding operation to discriminate between the inter-frame difference metric values and thus identify the video break points. Our identification approach is not threshold-based, using an automatic unsupervised distance classification procedure instead of a threshold. Thus, we provide a region-growing based classification approach, that proves to be very efficient in clustering the distances between feature vectors of consecutive frames. The two resulted distance classes determine a satisfactory video shot detection.     

Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream

The increased availability and usage of multimedia information have created a critical need for efficient multimedia processing algorithms. These algorithms must offer capabilities related to browsing, indexing, and retrieval of relevant data. A crucial step in multimedia processing is that of reliable video segmentation into visually coherent video shots through scene change detection. Video segmentation enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences generally contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. While existing algorithms perform relatively well for detecting abrupt transitions (video cuts), reliable detection of gradual changes is much more difficult. A novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on a compressed multimedia bitstream is proposed. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. Statistical sequential analysis is used to provide an unified framework for the detection of both abrupt and gradual scene changes.     

视频检索中镜头分割方法综述

视频序列的镜头分割亦称镜头变化检测是视频检索中的关键技术之一。镜头变化是指视频序列中场景内容的变化。该文介绍了目前镜头分割的常用方法,包括灰度分割法、边缘分割法、彩色直方图分割法、MPEG视频的分割方法、块匹配镜头分割方法、统计判决镜头分割方法、基于聚类的镜头分割方法、镜头渐变的检测等,指出了研究场景内容的表征方法、特征提取方法、特征的检测尺度以及稳健可靠的实用镜头分割方法是目前主要的研究方向。     

Fast scene change detection using direct feature extraction fromMPEG compressed videos

In order to process video data efficiently, a video segmentation technique through scene change detection must be required. This is a fundamental operation used in many digital video applications such as digital libraries, video on demand (VOD), etc. Many of these advanced video applications require manipulations of compressed video signals. So, the scene change detection process is achieved by analyzing the video directly in the compressed domain, thereby avoiding the overhead of decompressing video into individual frames in the pixel domain. In this paper, we propose a fast scene change detection algorithm using direct feature extraction from MPEG compressed videos, and evaluate this technique using sample video data, First, we derive binary edge maps from the AC coefficients in blocks which were discrete cosine transformed. Second, we measure edge orientation, strength and offset using correlation between the AC coefficients in the derived binary edge maps. Finally, we match two consecutive frames using these two features (edge orientation and strength). This process was made possible by a new mathematical formulation for deriving the edge information directly from the discrete cosine transform (DCT) coefficients. We have shown that the proposed algorithm is faster or more accurate than the previously known scene change detection algorithms     

基于多帧图像的视频文字跟踪和分割算法

视频中文字的提取是视频语义理解和检索的重要信息来源．针对视频中的静止文字时间和空间上的冗余特性,以文字区域的边缘位图为特征对检测结果作精化,并提出了基于二分搜索法的快速文字跟踪算法,实现了对文字对象快速有效的定位．在分割阶段,除了采用传统的灰度融合图像进行文字区域增强方法,还结合边缘位图对文字区域进行进一步的背景过滤．实验表明,文字的检测精度和分割质量都有很大提高．     

Motion Flow-Based Video Retrieval

In this paper, we propose the use of motion vectors embedded in MPEG bitstreams to generate so-called ldquomotion flowsrdquo, which are applied to perform video retrieval. By using the motion vectors directly, we do not need to consider the shape of a moving object and its corresponding trajectory. Instead, we simply ldquolinkrdquo the local motion vectors across consecutive video frames to form motion flows, which are then recorded and stored in a video database. In the video retrieval phase, we propose a new matching strategy to execute the video retrieval task. Motions that do not belong to the mainstream motion flows are filtered out by our proposed algorithm. The retrieval process can be triggered by query-by-sketch or query-by-example. The experiment results show that our method is indeed superb in the video retrieval process.     

AVCD-FRA: A novel solution to automatic video cut detection using fuzzy-rule-based approach

Video shot boundary detection (SBD) is a fundamental step in automatic video content analysis toward video indexing, summarization and retrieval. Despite the beneficial previous works in the literature, reliable detection of video shots is still a challenging issue with many unsolved problems. In this paper, we focus on the problem of hard cut detection and propose an automatic algorithm in order to accurately determine abrupt transitions from video. We suggest a fuzzy rule-based scene cut identification approach in which a set of fuzzy rules are evaluated to detect cuts. The main advantage of the proposed method is that, we incorporate spatial and temporal features to describe video frames, and model cut situations according to temporal dependency of video frames as a set of fuzzy rules. Also, while existing cut detection algorithms are mainly threshold dependent; our method identifies cut transitions using a fuzzy logic which is more flexible. The proposed algorithm is evaluated on a variety of video sequences from different genres. Experimental results, in comparison with the most standard cut detection algorithms confirm our method is more robust to object and camera movements as well as illumination changes.     

一种压缩域中的体育视频慢镜头探测方法

慢镜头探测是基于内容视频分析的一项重要技术.在介绍视频压缩的基本理论及MPEG-1视频压缩标准的基础上,对压缩城内的慢镜头探测进行了研究,提出了一种两阶段压缩域下的慢镜头探测方法.该探测方法的第一步是基于宏块类型信息的镜头探测,在镜头探测的基础上定义和计算镜头密度,探测可能的慢镜头出现位置;第二步是计算压缩域下连续帧间差,从候选慢镜头中探测慢镜头.实验结果显示,相对于已有的方法,该方法具有较快的探测速度和较高的准确率.     

Sudden scene change detection in compressed video using interpolated macroblocks in B-frames

This paper addresses an important area in video processing, namely compressed domain processing. For video indexing, video scene transition detection is an essential step to segment the video. Current techniques for scene change detection tend to suffer from a major limitation as most of them cannot identify scene transitions in the compressed domain. Since most video is expected to be stored in the compressed domain, scene transition detection in this domain is highly desirable. In this paper an algorithm for video scene change detection is proposed to overcome this limitation. In this scheme, properties of the B-frames are used as it is capable of measuring the correlation between two adjacent reference frames. The results show that this scheme performs better than schemes based on P-frames. Proposed scheme can be directly applied with compressed data with minimum decompression and hence it is computationally efficient and makes real time implementations possible. Results show that video scene transitions can be identified satisfactorily with the proposed scheme.     

从视频中检测人脸

视频中人脸检测的应用领域广泛，近来受到了极大关注。文中提出一种在MPEG流中检测人脸的新方法，它可以从复杂背景中有效地检测方向、大小不同的人脸，还可以处理多个人脸交叠的情况，为适应视频检索的需要，该算法依据帧间冗余性，自适应地调整肤色检测器，利用MPEG流中的运动矢量在一个GOP内跟踪人脸，依据场景的变化更新分割码本等措施，有效地提高了计算速度。用算法测试多个视频序列，实验结果令人满意。     

一种基于宏块类型匹配的镜头切换检测算法

提出了一种新的MPEG压缩视频镜头切换检测算法。该方法通过分析切换点附近各帧的宏块类型特征，引入了一种新的“宏块类型匹配符”（MTMO），使用其在宏块级别上度量子图片组（SGOP）内部各帧之间的差异性，并结合一种自适应阈值选取技术，完成镜头切换检测。实验结果表明，所提算法适用于不同SGOP结构的MPEG视频序列，查全率达99％，准确率达95％。     

Video parsing and browsing using compressed data

Parsing video content is an important first step in the video indexing process. This paper presents algorithms to automate the video parsing task, including partitioning a source video into clips and classifying those clips according to camera operations, using compressed video data. We have developed two algorithms and a hybrid approach to partitioning video data compressed according to the JPEG and MPEG standards. The algorithms utilize both the video content encoded in DCT (Discrete Cosine Transform) coefficients and the motion vectors between frames. The hybrid approach integrates the two algorithms and incorporates multi-pass strategies and motion analyses to improve both accuracy and processing speed. Also, we present content-based video browsing tools which utilize the information, particularly about the shot boundaries and key frames, obtained from parsing.     

电视录象图象的分类与检索

本文对现有的电视图象分类算法和检索算法作了的分析，并在图象分类方面提出了一种自适应分类算法；在检索方面提出了一种基于颜色的样本与非样本学习相结合的方法。自适应分类算法的计算量与相邻帧间的变化量成正比；对纡绝大多数相邻帧，只要计算数目极小的区域就可完成分类工作。实验证明，本文提出的分类与检索方法在速度与精度上均有明显的提高。     

基于多特征相似度曲线曲率检测的关键帧提取

网络多媒体的迅猛发展和普及使得对海量视频信息进行快速和低成本管理的需求日益迫切,而关键帧可以大大减少视频索引的数据量,同时也为查询和检索视频提供了一个组织框架。针对现有关键帧提取算法存在的特征选取单一、阈值选择困难和视频类型局限性等问题,提出了一种基于多特征相似度曲线最大曲率点检测的关键帧提取方法。算法利用多特征融合的相似性度量来捕获视频内容的显著变化,弥补了单一特征对视频内容描述不充分的不足,且基于滑动窗口的检测算法无需阈值选择,可以实时、局部地提取关键帧,解决了传统算法计算量大、通用性差的问题。最后通过实验利用一种保真度评估标准验证了该算法的有效性。