Image analysis – Pattern recognition – Classification
Reexamination Certificate
2001-08-01
2003-06-10
Tran, Phuoc (Department: 2621)
Image analysis
Pattern recognition
Classification
C382S305000, C375S240270, C348S180000
Reexamination Certificate
active
06577764
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The field of the invention relates to real time video processing, and, more specifically, to measurement of digital video image quality using principles of human physiological perception.
2. Background of the Technology
The future of image transmission—indeed, much of the present—is the streaming of digital data over high-speed channels. Streaming audio and video and other forms of multimedia technologies are becoming increasingly common on the Internet and in digital broadcast satellite television, and will take over most of the television broadcast industry in the next decade.
Broadcasters naturally want to build quality assurance into the product they send their customers. Such quality assurance is difficult, especially when video streams originate in a variety of different formats. Furthermore, various transmission channels have quite different degradation characteristics. Experts in video quality analysis and standardization communities have been and currently are grappling with this problem by assessing various methods of digital video quality assessment and correction in order to standardize quality measurement.
These considerations drive the search for the most objective mathematical and computational techniques to enable quality metrics. Ultimately, to be of any use, calculated quality measurements and the quality humans perceive during viewing must correlate. Mathematically modeling the visual pathways and perceptual processes inside the human body is a natural way to maximize this correlation.
Previous methods to computationally model the way humans judge visual quality relied on the lowest perceptual mechanisms, principally at the retinal level. A good example of these methods is edge detection, a visual function that takes place in the retina. There is an unmet need for visual quality measurement methods that model the higher functions of the human visual pathway in the visual cortex, the level at which the brain understands what is seen.
Specifically, a number of problems with the prior art exist in the regime of video quality analysis or measurement and the fundamental technique of video quality analysis with regard to digital video. One example in terms of digital video is what viewers often receive from a dish network, such as provided by Echostar Satellite of Littleton, Colo., or DirecTV® of El Segundo, Calif. Digital video is also what viewers typically see when working with a computer to, for example, view Internet streaming and other video over the Internet. Other examples of digital video include Quicktime™ movies, supported by Apple Computer, Inc., of Cupertino, Calif., AVI movies in Windows, and video played by a Windows media player. Another important example of digital video is high definition television (HDTV). HDTV requires a substantially greater amount of bandwidth than analog television due to the high data volume of the image stream.
What viewers currently watch, in general, on standard home television sets is analog video. Even though the broadcast may be received as digital video, broadcasts are typically converted to analog for presentation on the television set. In the future, as HDTV becomes more widespread, viewers will view digital video on home televisions. Many viewers also currently view video on computers in a digital format.
An unmet need exists in the prior art for a fundamental method of analyzing video quality. The need arises typically to address some type of degradation in the video. For example, noise may have been introduced in a video stream that causes the original picture to be disturbed. There are various types of noises, and the particular type of noise can be critical. For example, one form of digital video quality measurement involves examination of the specific type of degradation encountered.
Examples of various types of noise include the following. In one type of digital noise, the viewer sees “halos” around the heads of images of people. This type of noise is referred to as “mosquito noise.” Another type of noise is a motion compensation noise that often appears, for example, around the lips of images of people. With this type of noise, to the viewer, the lips appear to “quiver.” This “quivering” noise is noticeable even on current analog televisions when viewing HDTV broadcasts that have been converted to analog.
The analog conversion of such broadcasts, as well as the general transmittal of data for digital broadcasts for digital viewing, produces output that is greatly reduced in size from the original HDTV digital broadcast, in terms of the amount of data transferred. Typically, this reduction in data occurs as a result of compression of the data, such as occurs with a process called moving pictures expert group (MPEG) conversion or otherwise via lossy data compression schemes known in the art. The compression process selectively transfers data, reducing the transmittal of information among frames containing similar images, and thus greatly improving transmission speed. Generally, the data in common among these frames is transferred once, and the repetitive data for subsequent similar frames is not transferred again. Meanwhile, the changing data in the frames continues to be transmitted. Some of the noise results from the recombination of the continually transferred changing data and reused repetitive data.
For example, when a news broadcaster is speaking, the broadcaster's body may not move, but the lips and face may continuously change. The portions of the broadcaster's body, as well as the background behind the broadcaster on the set, which are not changing from frame to frame, are only transmitted once as a result of the compression routine. The continuously changing facial information is constantly transmitted. Because the facial information represents only a small portion of the screen being viewed, the amount of information transmitted from frame to frame is much smaller than would be required for transmission of the entire frame for each image. As a result, among other advantages, the transmission rate for such broadcasts is greatly increased from less use of bandwidth.
As can be seen from the above example, one type of the changing data that MPEG continuously identifies for transfer is data for motion occurring among frames, an important part of the transferred video. For video quality purposes, accurate detection of motion is important. Inaccuracies in identification of such motion, however, lead to subjective image quality degradation, such as lip “quivering” seen in such broadcasts.
There remains an unmet need to determine, using an objective technique, the quality of video streams in a manner that is consistent with human subjective opinion of video quality. There is a further unmet need to improve on the existing state of the art for making such objective assessments, in that none of the existing techniques has proven to be superior to analysis using peak signal-to-noise ratio (PSNR).
PSNR is a mathematical comparison of differences among video frames, once the frames have been reduced to numerical data, following capture and processing in a computer. For example, video that is operated upon, such as by undergoing transmission to a remote site for viewing, typically can undergo degradation in video quality. Such operations upon the video stream are generically referred to as “hypothetical reference circuits” (HRCs). Comparison may be made in this example between the original source video stream and the transmitted, possibly degraded video stream in order to determine the amount of degradation that has occurred.
In one existing method for subjectively measuring such possible degradation, the original frames or video sequences are shown to human observers, and then the possibly degraded frames or sequences are shown to the observers. The observers are then asked to rank the degradation on a scale, such as a scale of one to ten.
In one simple existing objective technique of video quality analysis, the numerical data that is produced by i
Myler Harley R.
Van Dyke-Lewis Michele
Kelber Steven B.
Piper Rudnick LLP
Sherali Ishrat
Teranex, Inc.
Tran Phuoc
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