A Correlation-Based No-Reference Packet-Loss Metric (original) (raw)

Artifacts Detection by Extracting Edge Features and Error Block Analysis from Broadcasted Videos

With the rapid development of the application of video surveillance and broadcast systems, the evaluation of video quality becomes an emerging research. Error detection is an important criterion to measure the quality of images/videos transmitted over unreliable networks particularly in the wireless channel. At the time of acquisition and transmission, video frames are always distorted by various artifacts. In many video processing applications, accurate knowledge of these errors or distortions present in the input video sequence is very important. In a real system, noises are mainly introduced by the camera and the quantization step of decoding process. But the distortions occur when the videos are transmitted through analog or digital medium. Some errors may be introduced when the analog video signal transmits in wired channel, but in wireless communication it cannot be ignored as occurring frequently. So, structure-oriented video distortions detection significantly impacts the effectiveness of video processing algorithms. Through the advancement of IPTV and HDTV technology, previously subtle errors in videos are now becoming more prominent because of the both structure oriented and compression based artifacts. To transmit high quality videos and images with low bit rate over the wireless channel, various compression algorithms are used. In B-DCT compression a slight change of luminance in border area can cause a step in the decoded image if the neighbor block fall into different quantization intervals. Therefore, the decompressed image and video exhibits various kind of artifacts. One of the most obtrusive artifacts is the ”Blocking Artifact”. Both compression and content based error occurs not only in video broadcasting but also surveillance systems during its transmission over wired and wireless channel. In this dissertation, we focus towards the development of a real-time video quality check system.To overcome the above issues we first propose a measurement system for compression based and broadcasted artifacts and then analyze the distortion patterns occurred during broadcasting over wireless channels. Firstly, The proposed system is achieved by a measurement of various artifacts of videos by analyzing the distribution of local properties of image signals like dominant edge magnitude and direction. We also propose a metric to detect damaged frame by considering the contextual information, such as their consistency and edge continuity. To achieve human vision measurement system we incorporate light weighted edge gradient magnitude information for video artifacts. According to the statistical information the distorted frames are then estimated based on the characteristics of their surrounding frames. Then we generate a criteria function to detect the distorted frame from sequence of video frames. Secondly, we propose a method to analyze the distortion pattern or block errors in video frames that occurred during its transmission and broadcasting over wireless channel. To achieve real-time performance we quantize edge gradient phase information of the image and histogram of the quantized values. By making the noise characteristics, we can filter out background pixels and compression based patterns. Then use the prominent texture patterns to classify them in different block errors and analyze them for error concealment. So, through accumulating histogram-based edge gradient information we can achieve height, width, shape and rotation of the distortion patterns of the video frames and analyze the distorted content pattern not only in video error detection application but also in error concealment, restoration and retrieval. Finally, evaluating the performance of all the mechanisms through extensive experiments on prominent datasets and broadcasted videos show that the proposed algorithm is very much efficient to detect errors for video broadcast and surveillance applications in terms of computation time and analysis of distorted frames.