You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Overview of Scene Change Detection- Application to 
Chapter Title:  REVIEW ON SCENE CHANGE DETECTION 
1. Abstract of Cited Paper (BIB001):  Scene change detection (SCD) is one of several fundamental problems in the design of a video database management system (VDBMS). It is the first step towards the automatic segmentation, annotation, and indexing of video data. SCD is also used in other aspects of VDBMS, e.g., hierarchical representation and efficient browsing of the video data. In this paper, we provide a taxonomy that classifies existing SCD algorithms into three categories: full-video-image-based, compressed-video-based, and model-based algorithms. The capabilities and limitations of the SCD algorithms are discussed in detail. The paper also proposes a set of criteria for measuring and comparing the performance of various SCD algorithms. We conclude by discussing some important research directions. 
2. Abstract of Cited Paper (BIB002):  This paper introduces a new method for video scene change detection. The proposed method utilizes the luminance histogram twice difference in order to determine the dynamic threshold needed to evaluate the break. The adaptive determination of the threshold, minimizes the number of incorrect decisions leading to a robust and accurate determination of the actual scene break. The method is simple, computationally attractive and capable of detecting changes in a variety of visual inputs. Experimental results indicate that the new method constantly outperforms existing techniques that are based on static thresholds. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  There is an urgent need to extract key information from video automatically for the purposes of indexing, fast retrieval, and scene analysis. To support this vision, reliable scene change detection algorithms must be developed. Several algorithms have been proposed for both sudden and gradual scene change detection in uncompressed and compressed video. In this paper some common algorithms that have been proposed for scene change detection are reviewed. A novel algorithm for sudden scene change detection for MPEG-2 compressed video is then presented. This uses the number of interpolated macroblocks in B-frames to identify the sudden scene changes. A gradual scene change detection algorithm based on statistical features is also presented. 
5. Abstract of Cited Paper (BIB005):  In order to manage the growing amount of video information efficiently, a video scene change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the scene change detection to organize the video content. In this paper, we present an effective scene change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate scene change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis. 
6. Abstract of Cited Paper (BIB006):  Several scene-change detection algorithms have been proposed in literature up to now. Most of them use fixed thresholds for the similarity metrics used to decide if there was a change or not. These thresholds are obtained by empirically or they must be calculated before the detection after the whole sequence is obtained. Performance of most scene change detectors decreases considerably for videos with high scene complexity and variation. In this paper, we review previous works in this area, and study the correlation between local statistical characteristics, scene duration and scene change. Based on this analysis, we further propose and implement a novel scene change detection method for H.264 codec, defining an automated, dynamic threshold model which can efficiently trace scene changes. Experimental results on QCIF videos indicate very good performance with significantly improved accuracy combined with minimum complexity.

Literature Review:
With the rapid growth of multimedia production and distribution, more and more people, concern the problem on copy protection of multimedia data. This problem become obvious as the explosive growth of the internet in recent few years. Video watermarking requires the scene change detection in the first step. Hence, this section presents a review of different scene change detection techniques of videos. Scene change detection algorithms are based on the pixel differences, compressed(MPEG-2)domains, temporal segmentation luminance histograms based framework for temporal segmentation, sudden scene change detection for MPEG-2 compressed video, algorithm using direct edge information extraction from MPEG video data is used. The comparison and analysis of scene change detection algorithms is described in the following subsections. Lock Yeo and Bede Liu et al proposed scene analysis which is in the form of temporal segmentation of videos and flashlight detection can be performed on compressed videos using only the dc coefficients of compressed domain  .The core information of videos is captured in the greatly reduced dc sequences, thus permitting these operations to be performed much more efficiently. But these temporal segmentation is done only on compressed video, not for uncompressed video. K. Tse et al presents scene change detection algorithms which is based on the pixel differences and compressed(MPEG-2) domains BIB001 .The main problem of this method is that it suffers from the variations incurred by camera motion. This algorithm has the potential to detect gradual scene changes. Haitao Jiang et al presents a Scene change detection Techniques for video database system which provide automatic segmentation, annotation, and indexing of video data BIB002 .It provide a taxonomy that classifies the existing algorithms into three categories: full video image based, compressed-video-based and model-based algorithms. P.Bouthemy et al gives Approach to scene change detection & characterization [8] .The Method relay on statistical technique robustness and efficiency for compressed video. The Image is represented in 2D affine model. Xinying Wang et al suggested a twice difference of luminance histograms based framework for temporal segmentation BIB003 .The method has been introduced to perform unsupervised.However, this method need to be further expended to detect complex transitions between scene change as fades and dissolve. Seong-Whan Lee has presented a method for scene change detection algorithm using direct edge information extraction from MPEG video data BIB004 .The paper proposed a fast scene change detection algorithm using direct feature extraction from MPEG compressed videos, and evaluate this technique using sample video data.This process was made possible by a new mathematical formulation for deriving the edge information directly from the discrete cosine transform coefficients. W. A. C. Fernando proposed an algorithm for sudden scene change detection for MPEG-2 compressed video BIB005 .This uses the number of interpolated macroblocks in B-frames to identify the sudden changes.This algorithm can detect abrupt scene changes irrespective of the nature of the sequences. Shu-Ching Chen1 et al BIB006 proposed a technique for uncompressed video data.The paper compare the segmentation mask maps between two successive video frames & Cannot be used for compressed video. Anastasios Dimou et alproposed a Scene change detection for H.264 BIB007 .It scribes the correlation between local statistical characteristics,scene duration and scene change and It uses only previous frames for the detection. Priyadarshinee Adhikari et al proposed a segmentation method based on colour difference histogram  .The paper describes a scene change detection method by scaling the histogram difference between the two frames. It provide the scaled frame difference that is dynamically compressed by log formula and it is more convenient to decide the threshold. It is based on detecting edges in two neighbouring images and comparing these images. Purnima.S.Mittalkod et al present a paper in which it gives the classification of shot boundary detection algorithms, including those that deal with gradual shot transitions  .This paper compares different Shot Boundary Detection Algorithms and Techniques & concluded that these techniques can be further refined and implemented for automatic shot detection. However, video analysis remains a challenging task with respect to unstructured home videos.