Topic
Video quality
About: Video quality is a research topic. Over the lifetime, 13143 publications have been published within this topic receiving 178307 citations.
Papers published on a yearly basis
Papers
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NEC1
TL;DR: In the encoding of an SDTV size video, the addition of range adjustment results in a reduction in the computational complexity of motion estimation of roughly 65%, while maintaining the same video quality.
Abstract: The paper presents a fast and accurate motion estimation algorithm. To obtain accurate motion vectors while minimizing computational complexity, we adjust the search range for each frame and each block to suit the motion level of the video. An appropriate search range for each frame is determined on the basis of motion vectors and prediction errors obtained for the previous frame. At each block, the search range is determined on the basis of the search range of its frame and of the motion vector values of all adjacent blocks for which those values have already been obtained. With our algorithm, since narrow search ranges are chosen for areas in which little motion occurs, computational complexity can be reduced without degrading estimation accuracy. Since wide search ranges are chosen for areas of significant motion, good video-quality encoding can be maintained. In the encoding of an SDTV size video, the addition of range adjustment results in a reduction in the computational complexity of motion estimation of roughly 65%, while maintaining the same video quality.
42 citations
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28 Nov 2006TL;DR: In this article, an estimation model specifying unit (112/212) and a specified estimation model (122/222) indicating the relationship between a video medium frame rate (/encoded bit rate) and the subjective video quality according to the input encoding bit rate (121B/input frame rate (221A), is presented.
Abstract: When subjectively estimating a video quality for main parameters (121/221) by inputting an input encoded bit rate (121B/221B) indicating an encoded bit quantity per unit time and an input frame rate (121A/221A) indicating a frame quantity per unit time for a video medium, an estimation model specifying unit (112/212) specifies an estimation model (122/222) indicating the relationship between a video medium frame rate (/encoded bit rate) and the subjective video quality according to the input encoding bit rate (121B/input frame rate (221A)), estimates the subjective video quality for the input frame rate (121A/input encoded bit rate 221B) by using the specified estimation model (122/222), and outputs it as an estimated value (123/223).
41 citations
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TL;DR: The Institute for Telecommunication Sciences has developed a subjective test method to determine a person's ability to perform recognition tasks using video, thereby rating the quality according to the usefulness of the video quality within its application.
Abstract: To develop accurate objective measurements (models) for video quality assessment, subjective data is traditionally
collected via human subject testing. The ITU has a series of Recommendations that address methodology for performing
subjective tests in a rigorous manner. These methods are targeted at the entertainment application of video. However,
video is often used for many applications outside of the entertainment sector, and generally this class of video is used to
perform a specific task. Examples of these applications include security, public safety, remote command and control,
and sign language. For these applications, video is used to recognize objects, people or events. The existing methods,
developed to assess a person's perceptual opinion of quality, are not appropriate for task-based video. The Institute for
Telecommunication Sciences, under a program from the Department of Homeland Security and the National Institute for
Standards and Technology's Office of Law Enforcement, has developed a subjective test method to determine a person's
ability to perform recognition tasks using video, thereby rating the quality according to the usefulness of the video
quality within its application. This new method is presented, along with a discussion of two examples of subjective tests
using this method.
41 citations
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11 Jul 2010
TL;DR: In this article, a subjective experiment was conducted in which human observers rated the annoyance of the distortions in the videos and found that distortion in a salient region is indeed perceived as much more annoying as compared to distortions in a non-salient region.
Abstract: In this paper, distortions caused by packet loss during video transmission are evaluated with respect to their perceived annoyance. In this respect, the impact of visual saliency on the level of annoyance is of particular interest, as regions and objects in a video frame are typically not of equal importance to the viewer. For this purpose, gaze patterns from a task free eye tracking experiment were utilised to identify salient regions in a number of videos. Packet loss was then introduced into the bit stream such as that the corresponding distortions appear either in a salient region or in a non-salient region. A subjective experiment was then conducted in which human observers rated the annoyance of the distortions in the videos. The outcomes show a strong tendency that distortions in a salient region are indeed perceived as much more annoying as compared to distortions in the non-salient region. The saliency of the distorted image content was further found to have a larger impact on the perceived annoyance as compared to the distortion duration. The findings of this work are considered to be of great use to improve prediction performance of video quality metrics in the context of transmission errors.
41 citations
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28 Apr 1995TL;DR: Experimental data shows the peak-detection algorithm based on the median filter is effective even for video with significant motion or sudden light changes such as in action movies, or sports video.
Abstract: This paper presents a peak-detection algorithm based on the median filter. When used with difference or correlation measures between contiguous video frames, this algorithm can determine significant peaks at "abrupt scene changes" in MPEG compressed video. Experimental data shows the algorithm is effective even for video with significant motion or sudden light changes such as in action movies, or sports video.
41 citations