An Optimized Dynamic Scene Change Detection Algorithm for H.264/AVC Encoded Video Sequences

Increasing the demands of digital video by developing the Internet market and the multimedia technologies, video indexing technique such as scene change detection is required to manage the data efficiently. In conventional methods, scene change is detected by comparing the value of current detected measure with the fixed threshold induced from preceding experiments. However, this can not guarantee the best performances on all various video sequences, due to their own specific characteristics. To solve this problem, a novel adaptive threshold decision method is proposed. First, histogram of scene change detection measure induced from preceding experiments is derived. Then this is modeled with log-normal distributed pdf and the model parameters are estimated. Consequently, experimental results obtained from this pdf model and estimated parameters demonstrate better performance of the proposed method comparing with conventional methods.

Video is an important and challenging medium and requires sophisticated indexing schemes for efficient retrieval from visual databases. An important step in video indexing is scene change detection. Recently, several scene change detection algorithms in the pixel and compressed (MPEG-2) domains have been reported in the literature. These algorithms are computationally complex and are not very robust in detecting gradual scene changes. The authors propose an efficient technique for detecting scene changes in the MPEG-2 compressed domain. The proposed algorithm has the advantage of fast scene change detection. In addition, this algorithm has the potential to detect gradual scene changes.

Scene change detection is the process of identifying the differences between the multi-temporal image scenes at the semantic level, which has significant potential in the application of urban development and land management. In this paper, we propose a novel deep convolution canonical correlation analysis neural network (DCCANet) architecture, which could consider the spectral-spatial-temporal correlation feature for scene change detection in remote sensing images. For this purpose, we put together the convolutional neural networks (CNNs) and the deep canonical correlation analysis (DCCA) into the end-to-end network. The CNN could get the spectral-spatial feature information for scene representation, while the later could enhance the temporal correlation by nonlinear high- dimensional transformations between the multi-temporal image scenes for scene change detection. Experiments with high-resolution remote sensing image scene datasets demonstrated that our proposed approach can get a better performance in scene classification and change detection.

대용량 컨텐츠 수요와 공급의 증가에 따라 UHD 비디오의 분석, 색인, 편집 등을 위한 장면전환을 검출하는 방법이 요구되고 있다. 이전까지 많은 연구자들이 다양한 장면전환 검출 방법을 연구해왔지만 카메라의 다양한 움직임과 장면의 변화를 정확하게 검출하기는 어려웠다. 또한, 4K (3820x2160) 해상도 이상의 UHD 비디오들은 데이터 량을 더욱더 증가시키기 때문에 이전의 장면전환 검출 방법은 UHD 비디오 컨텐츠에 적용하기에는 너무 많은 시간이 걸리는 문제점이 발생한다. 따라서, 압축률이 높은 차세대 고효율 코덱 HEVC를 이용하여 장면전환을 검출하는 방법이 요구되고 있다. 본 논문에서는 차세대 고효율 코덱 HEVC의 부호화 부가정보를 이용한 4가지 장면전환 검출 방법을 제안하며, 대용량 비디오의 장면전환 검출을 위한 픽셀 기반의 새로운 장면전환 검출 시스템 구조를 제안한다. 연산량을 줄이기 위해 프레임 특징에 따른 방법을 각각 제시하며, 실험 결과를 통해 HEVC로 부호화 된 UHD 컨텐츠들의 장면전환 검출 가능성을 확인한다. With the increase of mass contents data, a method of a scene change detection is required for analysis, indexing and editing. Although many researchers are studying a variety of scene change detection method, it is too difficult to accurately detect various movements of the cameras and scene changes. Also, earlier scene change detection methods take too much time to apply to UHD video contents. That is because the UHD video contents with 4K (3820x2160) resolution or higher have greater amount of data. Therefore a method for detecting a scene change by using the next-generation codec, HEVC, is required. In this paper, we propose four scene change detection methods using the coding additional information of HEVC, and a new pixel-based scene change detection system. Furthermore, through the experimental results, we check the possibility of detecting the scene changes of UHD videos encoded in HEVC format.

An efficient video de-interlacing technique with scene change detection is proposed and its performances are examined. Scene changes happen quite often in film broadcasting and they tend to destabilize the quality of performance such as jagged effect, blurred effect, and artifacts effect, while de-interlacing technique is utilized. Therefore, the issue of scene change needs to be addressed with de-interlacing process. In the proposed method, de-interlacing begins with scene change detection, which is to ensure that the interfield information is used correctly. To improve the quality of de-interlacing, the factors of scene change are taken into account when de-interlacing techniques are applied. The simulation results show that the proposed algorithm exhibits better performances than other interpolation algorithms

An algorithm is proposed to detect abrupt and gradual scene changes in H.264 compressed videos. In H.264, the inter prediction in the encoding process generates the DC component of the prediction residual. In this paper, we use the DC component of inter prediction residuals as well as motion vectors to detect scene changes. Using the proposed algorithm, we can detect scene changes for H.264 compressed videos more effectively, as demonstrated by simulations.

This study proposes a framework for describing a scene change using natural language text based on indoor scene observations conducted before and after a scene change. The recognition of scene changes plays an essential role in a variety of real-world applications, such as scene anomaly detection. Most scene understanding research has focused on static scenes. Most existing scene change captioning methods detect scene changes from single-view RGB images, neglecting the underlying three-dimensional structures. Previous three-dimensional scene change captioning methods use simulated scenes consisting of geometry primitives, making it unsuitable for real-world applications. To solve these problems, we automatically generated large-scale indoor scene change caption datasets. We propose an end-to-end framework for describing scene changes from various input modalities, namely, RGB images, depth images, and point cloud data, which are available in most robot applications. We conducted experiments with various input modalities and models and evaluated model performance using datasets with various levels of complexity. Experimental results show that the models that combine RGB images and point cloud data as input achieve high performance in sentence generation and caption correctness and are robust for change type understanding for datasets with high complexity. The developed datasets and models contribute to the study of indoor scene change understanding.

내용기반 검색을 위한 비디오 데이터 장면전환 검출에서 점진적인 전환을 검출하는 것은 갑작스런 전환을 검출하는 것에 비해 일반적으로 어려운 문제로 알려져 있다. 본 논문에서는 가변형 동적 임계값과 가장 최근에 검출된 컷 프레임과 현재 프레임간의 특징값 차이인 컷 프레임 차를 이용하여 갑작스런 전환과 점진적인 전환을 찾아내는 기법을 제안한다. 이를 위하여 본 논문에서는 점진적인 전환이 갖는 특성과 수학적 모델을 제시하고 컷 프레임 차를 이용하여 점진적인 전환을 검출할 수 있음을 보인다. 그리고 이를 바탕으로 갑작스런 전환과 점진적인 전환을 함께 검출할 수 있는 방법을 제시한다. 실세계 동영상 데이터를 대상으로 한 실험을 통해 제안하는 기법이 점진적인 전환 효과의 종류에 종속적이지 않으며 적은 연산 비용으로 쉽게 점진적인 전환 유무를 검출 할 수 있음을 보인다. Gradual scene change detection is known as more difficult problem then abrupt scene change detection on video data analysis for contents based retrieval. In this paper, we present a new method for scene change detection far both abrupt and gradual change using the variable dynamic threshold arid cut frame difference (CFD). For this, We present the characteristics arid mathematical models of gradual transitions anti then, how can be detected by the CFD. And also we present new scene change detection algorithm based on cut frame difference. By the experimental result using real world video data indicate that the proposed method detect various scene changes both abrupt and gradual change efficiently without time-consuming computation and any dependency on a kind of gradual change effects.

In order to fulfill the requirement of limited channel bandwidth and of growing video demand like streaming media delivery on internet, and digital library, video compression is necessary. In video compression, temporal redundancy between adjacent frames is removed with block based motion estimation algorithms. Video represents a sequence of frames captured from camera. Scene is a series of consecutive frames captured from narrative point of view. In this paper we present an effective scene change detection method for an uncompressed video. We have divided frames in to blocks and applied a canny edge detector in consecutive frames. Count no of pixels (ones) in each block and compare it with consecutive frames. If scene change happens then number of pixels per block will change, based on that change we can detect scene change in consecutive frame. Here we have presented a hybrid approach, in which we have used scene change detection along with block based motion estimation algorithms (BME) to compress video.

Partitioning video sequences into individual shots is one of the fundamental processes in video content parsing and content-based video retrieval. Up to now, a variety of algorithms and systems have been developed to perform this task. However, most of these algorithms exhibit their weakness when applied to detect gradual transitions such as dissolves, wipe, fade-in and fade-out. In this paper, we presented an integrated scheme to the detection of abrupt camera breaks and gradual scene changes using DCT coefficients and motion data encoded in the MPEG compression stream. The core of the proposed approach is a tree-like classifier. Three algorithms are organized in the classifier to deal with the complicated situation in real-world video sequences separately.

In this paper, we propose a new scene change detection method based on multi-histogram for frame rate up-conversion. The proposed method manages multiple per-block histograms to extract the locality of image change. Thus, it can detect local scene change as well as global scene change between frames. Experiments show that the proposed method improves by 14.05dB the image quality of the interpolated frame with the local scene change.

Video scene change detection has great importance of managing and analyzing large amount of videos. Traditionally this technique used for indexing, segmenting and categorizing different types of videos. Very few works addressed to classify obscene using scene change detection method. In this research we proposed a simple approach for sensing objectionable videos by observing scene changes into different video genres. Video scenes are grouped into set of key frames. After analyzing duration of each scene and counting the number of key frames of designated scene, it has been shown that obscene videos have infrequent scene changing nature. While in sports, dramas, music and action films have large number of scene changes. We used six types of video genres and the decision has been made by setting a threshold based on extracted key frames. Experimental result showed that the accuracy is 83.33% and false positive rate is 16.67%.

Rate control plays a key role in a video encoder, and it has been a challenging research topic. In this paper, an adaptive variable length group of picture (GOP) level rate control algorithm for H.264 based on scene change detection is proposed. In the proposed algorithm, the length of current GOP changes adaptively with the occurrence of scene change, so that the entire sequence contains a fixed number of I frame to avoid the actual bit rate increasing. Furthermore, it adjusts the QP of the scene change frame to ease the pressure of the buffer. Simulation results show that the proposed algorithm improves the video quality significantly on the basis of meeting bit rate constraints accurately, which shows better adaptability for sequences with scene change occurrence frequently. When compared with existed schemes, the average peak signal-to-noise ratio (PSNR) improvement is more than 1.4 dB.

A common framework for rapid scene analysis for detecting scene changes in compressed motion JPEG and MPEG videos is proposed. We develop algorithms to detect both abrupt and gradual scene changes. The algorithms operate directly on the DC sequence which can be easily extracted from motion JPEG and MPEG compressed video without decompression. The DC images capture most of the essential "global" information, but is of a small fraction of the original data size. Operating on these images offers significant computation savings. Experimental results show that the proposed algorithms are fast and effective in detecting abrupt scene changes and gradual transitions.

In HEVC, bit-allocation model is based on the hierarchical control, which can divide video sequence into three levels: Group of Picture (GOP), frame and Coding Tree Unit (CTU). However, the fixed size of GOP fails to consider the influence of scene change in the video coding process, which may decrease the compression efficiency and reconstructed quality. In this paper, the main idea of the proposed algorithm is to detect the scene change efficiently, and then apply it in the rate control algorithm of HEVC to decrease the BD-rate and save the coding time.