Image analysis – Image compression or coding – Pyramid – hierarchy – or tree structure
Reexamination Certificate
2000-03-03
2004-05-04
Do, Anh Hong (Department: 2721)
Image analysis
Image compression or coding
Pyramid, hierarchy, or tree structure
C382S260000, C348S225100
Reexamination Certificate
active
06731815
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates to video compression, and more particularly to human vision based pre-processing for MPEG video compression that reduces the input bandwidth to a video compressor in a way that is visually optimal.
Video signals are usually characterized in three dimensions, i.e., two-dimensional spatial and temporal domains. The signal may be separated into luminance and chrominance component signals. The luminance component signal requires more bandwidth for faithful representation by video compression systems. Much research has been done to take the Human Vision System into account when designing video (image) compression algorithms. In “Adaptive Quantization of Picture Signals Using Spatial Masking”, Arun N. Netravali and Birendra Prasada, Proceedings of the IEEE, Vol. 65, No. 4, April 1977, pp. 536-548, and “MPEG2 Test Model 5, Adaptive Quantization”, ISO-IED/JTC1/SC29/WG11, April 1993, spatial masking by using luminance activity is used for bandwidth reduction. In “A Perceptually Tuned Subband Image Coder Based on the Measure of Just-Noticeable-Distortion Profile”, Chun-Hsien Chou and Yun-Chin Li, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 5, No. 6, December 1995, pp. 467-476, a more sophisticated subband image coder is described.
The Human Vision System is very adaptive to cope with the wide dynamic range of light intensity variations. Extensive research work has been done in this area, although most used simple stimuli. For complex signals “Intensity-Dependent Spatial Summation”, Tom N. Cornsweet and John I. Yellott, Jr., J. Opt. Soc. Am., Vol. 2, No. 10, October 1985, pp. 1769-1786, “Asynchrony in Image Analysis: Using the Luminance-to-Response-Latency Relationship to Improve Segmentation”, Pierre-Yves Burgi and Tierry Pun, J. Opt. Soc. Am., Vol. 11, No. 6 June 1994, pp. 1720-1726, “Human Luminance Pattem-Vision Mechanisms: Masking Experiments Require a New Model”, John M. Foley, J. Opt. Soc. Am., Vol 11, No. 6, June 1994, pp. 1710-1719, and the Chou et al article mentioned above show some very interesting insight to the process of light adaptation and contrast gain control. The adaptation process may be viewed as contrast gain control of the human eye's light sensing mechanism. The gain depends on the local as well as global (surround) light level. For complex signals there is a light center that represents the range of light intensity which is optimal for the particular complex signal. Local variation of the light level means that the sensor efficiency is degraded. A phenomenon, such as simultaneous contrast, may be explained in this way. The visibility of the same spatial pattern may be very different when viewed under different surrounds, see “Brightness Perception in Complex Fields”, C. J. Bartleson and E. J. Breneman, J. Opt. Aoc. Am., Vol. 57, July 1967, pp. 953-957.
What is desired is human vision based pre-processing for MPEG video compression that uses light intensity which offers the possibility of reducing visual redundancy while keeping the distortion in the spatial domains as well as the temporal domain to a minimum.
BRIEF SUMMARY OF THE INVENTION
Accordingly the present invention provides human vision based pre-processing for MPEG video compression that uses light intensity. An input video signal is input to a contrast gain control circuit where a Gaussian pyramid is constructed. The reduced pyramid image serves as a local light level. The reduced pyramid image also is processed to obtain a global light center. The difference between the local light level and the global light center is used as an index to a lookup table that provides a local gain control signal. The local gain control signal and a global gain control signal input to the contrast gain control circuit are used to multiply a Laplacian image derived from the input image, the resulting modified Laplacian image being subtracted from an equivalent lowpass filtered video image to produce a pre-processed output video signal having reduced bandwidth for input to an MPEG2 compressor.
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“Asynchrony in Image Analysis: Using the Luminance-To-Response-Latency Relationship To Improve Segmentation” Pierre-Yves Burgi and Thierry Pun, Journal of the Optical Society of America A, Optics, Image Science, and Vision, vol. 11, No. 6/ Jun. 1994.
“The Laplacian Pyramid as a Compact Image Code”, Peter J. Burt and Edward H. Adelson, IEEE Transactions on Communications, vol., Com-31, No. 4 Apr. 1983.
“Brightness Perception in Complex Fields”, C.J. Bartleson and E.J. Breneman, Journal of The Optical Society of America, vol. 57, No. 7, Jul. 1967.
A Perceptually Tuned Subband Image Coder Based on the Measure of Just-Noticeable-Distortion Profile, Chun-Hsien Chou and Yun-Chin Li, IEEE Transaction on Circuits and Systems for Video Technology, vol. 5, No. 6, Dec. 1995.
“Adaptive Quantization of Picture Signals Using Spatial Masking”, Arun N. Netravali and Birendra Prasada, Proceedings of the IEEE, vol. 65, No. 4 Apr. 1977.
“Intensity-Dependent Spatial Summation”, Tom N. Cornsweet and John I. Yellott, Jr., 1985 Optical Society of America, vol. 2, No. 10 Oct. 1985.
Human Luminance Patern-Vision Mechanisms: Masking Experiments Require a New Model, John M. Foley, 1994 Optical Society of America, vol. 11, No. 6 Jun. 1994.
Do Anh Hong
Gray Francis I.
Tektronix Inc.
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