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Detection of Betacam dropout defects that can occur in the digitisation process of old archived media has importance in the restoration of degraded data to a higher quality. Most of the existing methods rely on the temporal information of multiple consecutive frames to detect Betacam dropouts, which sometimes may not work well as several successive frames may contain a Betacam error at the same position. In this study, an automatic method of Betacam dropout error detection is proposed based on vertical patterns in a single frame. Hence, it is also applicable when temporal information-based detectors fail. The results of performance tests done in real working environments demonstrate that the proposed Betacam dropout detection method performs much better than the existing methods.

The quality of digital content has become increasingly significant in the digitalized broadcasting world. These days, consumers react even to subtle defects in media content, which in turn, influence consumer satisfaction about the entire content. The development of digital broadcasting technology has replaced tape-based content with file-based content. Nonetheless, in the process of generating file-based content, people are often confronted with different types of errors. Detecting such errors and fixing them requires a substantial amount of time and human labor, while being unable to fix them might lead to broadcast failure. Therefore in this paper, we introduce an automated restoration system that reduces intensive human labor in fixing the errors in the content generation process. Our automated video restoration system can be applied to different types of classical errors. We developed several algorithms to restore each error in the digital content derived from the KBS video archive. Implementing our method using a tool already familiar to content producers has also been one of our considerations. We are developing the restoration system as a plug-in to a well-known NLE (Non-linear editing system).

Identify a set of DCT coefficients that can be used in digital dropout error classification.A weighted neighborhood sampling strategy based on spatially correlated directional behavior.Feature extraction in DCT domain, resulting in lower time complexity and computational load.Correlates highly with human subjective judgments of quality of error. Digitization of old archived media is of great importance to preserve the originality of medium in terms of historical record as well as the means to quality improvement for reproduction purposes. However, digitization increases the exposure of the media to digital dropout error, thus presenting a significant degradation in perceptual quality of the converted video sequences. A numbers of mechanisms were investigated in the past to make these converted media more robust against digital dropout errors. Nevertheless, these techniques achieved little success, forcing manual quality check to assure standard quality. This paper presents an automatic solution to this problem based on discriminant DCT coefficients. Here, the idea is to build a block classification model by learning discriminant DCT coefficients first and utilize these coefficients along with an weighted neighborhood sampling strategy to formulate discriminant block descriptor so that within-class difference of the block features is minimized and between-class difference is maximized. This spatial detection is free from motion computation; thus performs accurately in presence of pathological motion (PM) and fast moving objects. Finally, the proposed method is compared against the existing methods to demonstrate improved detection accuracy using real degraded video archives.

With the rapid development of visual digital media, the demand for better quality of service has increased the pressure on broadcasters to automate their error detection and restoration activities for preserving their archives. Digital dropout is one of the defects that affect archived visual materials and tends to occur in block by block basis (size of 8 × 8). It is well established that human visual system (HVS) is highly adapted to the statistics of its visual natural environment. Consequently, in this paper, we have formulated digital dropout detection as a classification problem which predicts block label based on statistical features. These statistical features are indicative of perceptual quality relevant to human visual perception, and allow pristine images to be distinguished from distorted ones. Here, the idea is to extract discriminant block statistical features based on discrete cosine transform (DCT) coefficients and determine an optimal neighborhood sampling strategy to enhance the discrimination ability of block representation. Since this spatial frame based approach is free from any motion computation dependency, it works perfectly in the presence of fast moving objects. Experiments are performed on video archives to evaluate the efficacy of the proposed technique.

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearancebased feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.

We propose a pixel similarity based Betacam dropout detection method. Proposed method aims at detecting errors, having vertically similar pattern without considering sequential frame when archiving a tape-based content to a file-based system. Moreover, this method works along with a real-time instant Quality Control(QC) system with less false-alarm.

A spatial detection technique, based on the extrema of current pixel's neighborhood, is proposed for Digital Dropout error, evident in the archived media. Digital dropout is a major type of damage occurred due to the physical error in original tape and tends to occur in block by block basis. As long as the presence of fast moving object is observed in video sequences where current state-of-the-art methods fail to detect accurate error information, such spatial detection offers a much better solution in terms of quality and complexity. Experiments are performed on video archives to evaluate the efficacy of the proposed technique.

In this paper, we present an adaptive detection technique for blotch error, evident in old archived media. Traditional pixel based blotch detection methods, due to sensitivity of threshold, fail to detect blotch, if present, at identical location in successive frames. Furthermore, as the amount of archive data is quite large, processing time needs to be considered. To alleviate problems associated with traditional methods and speed up the process, in this paper, we have proposed a cloud computing solution where the blotch detector is a five frame based Rank order difference (ROD) detectors. False alarm is reduced by integrating adapting refinement based on local neighborhood statistics of candidate blotch regions in spatio-temporal domain. Experiment is performed on real archives to evaluate the efficacy of proposed solution.

Now-a-days, recent trend is to represent high quality images or videos by using less bit representation. To represent high quality videos or images with low bit rate, an effective compression algorithm removes the redundancy because of statistical correlation and also the insignificant component of image signal. This paper represents a new algorithm to measure the blocking artifacts of videos by analyzing the distortions of local properties of image signals like dominant edge magnitude and direction. For this purpose sobel convolution mask is used rather than kirsch mask to make the detection process faster and to model video noises that occur in broadcasting systems. Extensive experiments on various videos show that the new algorithm is very much efficient to measure the blocking artifacts in real time video error detection applications. © 2011 IEEE. Refereed/Peer-reviewed

To represent high quality videos or images with low bit rate, an effective compression algorithm removes the redundancy because of statistical correlation and also the insignificant component of image signal. This paper represents a new algorithm to measure the blocking artifacts of videos by analyzing the distortions of local properties of image signals like dominant edge magnitude and direction. Extensive experiments on various videos show that the new algorithm is very much efficient and faster to measure the blocking artifacts in real time video error detection applications.

This paper presents an edge extraction based automatic algorithm for detection of moving objects that have been specially developed to deal with the variations in illumination and background. We develop an efficient approach for background edge generation as well as update and robust method of edge matching that is able to effectively reduce the risk of false alarm. The proposed method can be successfully realized in various monitoring systems like intrusion detection as well as video surveillance. Experiments with real image sequences are presented, along with comparisons with some other existing methods, illustrating the advantages of the proposed algorithm.

Recently, the IDS(Intrusion Detection System) with a use of a video camera is an important part of the home security systems which start gaining popularity. However, the video intruder detection has not been widely used in the home surveillance systems due to its unreliable performance in an environment with an abrupt change in illumination. In this study, an effective moving edge extraction algorithm from a sequential image is proposed. The proposed algorithm extracts edge segments from the current image and eliminates the background edge segments by matching them with reference edge list, which is updated at every frame, to find the moving edge segments. The test results show that it can detect the contour of a moving object in such a noisy environment as there is an abrupt change in illumination.