US5764307A
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Method and apparatus for spatially adaptive filtering for video encoding
Assignee: Motorola Solutions Inc
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Filing: 1995-07-24
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Granted: 1998-06-09
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Expires: 2015-07-24
Abstract
Abstract The present invention provides a method (200) and an apparatus (100) for spatially adaptive filtering for video encoding. The apparatus filters a video sequence prior the encoding process. The apparatus comprises a noise variance determiner (102), a local variance determiner (104), a noise visibility function determiner (106), a Gaussian kernel determiner (108), and a convolver (110). The apparatus removes noise directly from a Displaced Frame Difference, DFD, signal. This novel approach removes noise and miscellaneous high frequency components from the DFD signal without the introduction of the filtering artifacts characteristic of current techniques. By reducing the miscellaneous high frequency components, the present invention is capable of reducing the amount of information that must be encoded by the video encoder without substantially degrading the decoded video sequence.
Inventors
- 👤 Taner Ozcelik
- 👤 James C. Brailean
- 👤 Aggelos K. Katsaggelos
- 👤 Ozan Erdogan
- 👤 Cheung Auyeung
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-28 13:08
CPC Classifications
H; H04; H04N; H04N19/00; H04N19/80; H04N5/00; H04N5/14; H04N5/21; H04N5/213
Claims Analysis
Total Claims: 26
Claim 1
Independent Claim
139 words
A method of spatially adaptive filtering a displaced frame difference, a DFD, the DFD is a difference between a video frame and an estimated video frame, the method comprising: determining, using a noise variance determiner, a noise variance of the DFD based on a predetermined signal-to-noise ratio; determining, using a local variance determiner, a local variance of the DFD based on the noise variance and the predetermined signal-to-noise ratio; determining, using a noise visibility determiner, a noise visibility function based on the noise variance, the local variance, and a predetermined image type; determining, using a Gaussian kernel determiner, a Gaussian kernel for every pixel in the DFD based on the noise visibility function, a predetermined normalizing coefficient, and a predetermined base variance; and convolving, using a convolver, the DFD with the Gaussian kernel to provide an adaptively filtered DFD.
Claim 2
Independent Claim
139 words
A method of spatially adaptive filtering a displaced frame difference, a DFD, the DFD is a difference between a video frame and an estimated video frame, the method comprising: determining, using a noise variance determiner, a noise variance of the DFD based on a predetermined signal-to-noise ratio; determining, using a local variance determiner, a local variance of the DFD based on the noise variance and the predetermined signal-to-noise ratio; determining, using a noise visibility determiner, a noise visibility function based on the noise variance, the local variance, and a predetermined image type; determining, using a Gaussian kernel determiner, a Gaussian kernel for every pixel in the DFD based on the noise visibility function, a predetermined normalizing coefficient, and a predetermined base variance; and convolving, using a convolver, the DFD with the Gaussian kernel to provide an adaptively filtered DFD.
Claim 3
Dependent Claim
50 words
The method of claim 1 wherein the noise variance is determined by calculating an energy contained in each predefined block using the following equation: ##EQU6## where M and N are the dimensions of the predefined block and i,j denotes a row and column of a pixel in the predefined block.
Claim 4
Dependent Claim
44 words
The method of claim 1 wherein the local variance of the DFD is determined using the following equation: ##EQU7## where M and N are the dimensions of the predefined block and i,j denotes a row and column of a pixel in the predefined block.
Claim 5
Dependent Claim
38 words
The method of claim 1 wherein the noise visibility function is determined using the following equation: ##EQU8## where σ.sub.ƒ 2 is the local variance, σ n 2 is the noise variance, and Θ is the predetermined image type.
Claim 6
Dependent Claim
50 words
The method of claim 1 wherein the Gaussian kernel is determined using the following equation: ##EQU9## where σ g 2 is the predetermined base variance, Z is the predetermined normalizing coefficient, υ(Θ) is the noise visibility function, and m,n denotes a row and column of a pixel in the DFD.
Claim 7
Dependent Claim
39 words
The method of claim 1 wherein the DFD convolved with the Gaussian kernel is determined using the following equation: ##EQU10## where g(m,n) is the Gaussian kernel and m,n denotes a row and column of a pixel in the DFD.
Claim 8
Dependent Claim
26 words
The method of claim 1 wherein the steps of the method are embodied in a Digital Signal Processor, DSP, that has been programmed with said steps.
Claim 9
Dependent Claim
25 words
The method of claim 1 wherein the steps of the method are embodied in an Application Specific Integrated Circuit, ASIC, arranged to provide said steps.
Claim 10
Dependent Claim
25 words
The method of claim 1 wherein the steps of the method are embodied in a gate array that has been arranged to provide said steps.
Claim 11
Dependent Claim
17 words
The method of claim 1 wherein the steps of the method are stored in a computer memory.
Claim 12
Dependent Claim
31 words
The method of claim 10 wherein the computer memory is a computer diskette having a stored program to impart processing functionality for implementing the steps of the method by the computer.
Claim 13
Dependent Claim
34 words
The method of claim 10 wherein the computer memory is a memory unit of the computer that includes a program to impart processing functionality for implementing the steps of the method by the computer.
Claim 14
Independent Claim
215 words
An apparatus for spatially adaptive filtering a displaced frame difference, a DFD, the DFD is a difference between a video frame and an estimated video frame, the apparatus comprising: a noise variance determiner, operably coupled to receive the DFD and a predetermined signal-to-noise ratio, for determining a noise variance of the DFD based on the predetermined signal-to-noise ratio; a local variance determiner, operably coupled to the noise variance determiner and operably coupled to receive the DFD, for determining a local variance of the DFD based on the noise variance and the predetermined signal-to-noise ratio; a noise visibility determiner, operably coupled to the noise variance determiner and the local variance determiner and operably coupled to receive a predetermined image type, for determining a noise visibility function based on the noise variance, the local variance, and the predetermined image type; a Gaussian kernel determiner, operably coupled to the noise visibility determiner and operably coupled to receive a predetermined base variance, for determining a Gaussian kernel for every pixel in the DFD based on the noise visibility function, a predetermined normalizing coefficient, and the predetermined base variance; and a convolver, operably coupled to the Gaussian kernel determiner and operably coupled to receive the DFD, for convolving the DFD with the Gaussian kernel to provide an adaptively filtered DFD.
Claim 15
Independent Claim
215 words
An apparatus for spatially adaptive filtering a displaced frame difference, a DFD, the DFD is a difference between a video frame and an estimated video frame, the apparatus comprising: a noise variance determiner, operably coupled to receive the DFD and a predetermined signal-to-noise ratio, for determining a noise variance of the DFD based on the predetermined signal-to-noise ratio; a local variance determiner, operably coupled to the noise variance determiner and operably coupled to receive the DFD, for determining a local variance of the DFD based on the noise variance and the predetermined signal-to-noise ratio; a noise visibility determiner, operably coupled to the noise variance determiner and the local variance determiner and operably coupled to receive a predetermined image type, for determining a noise visibility function based on the noise variance, the local variance, and the predetermined image type; a Gaussian kernel determiner, operably coupled to the noise visibility determiner and operably coupled to receive a predetermined base variance, for determining a Gaussian kernel for every pixel in the DFD based on the noise visibility function, a predetermined normalizing coefficient, and the predetermined base variance; and a convolver, operably coupled to the Gaussian kernel determiner and operably coupled to receive the DFD, for convolving the DFD with the Gaussian kernel to provide an adaptively filtered DFD.
Claim 16
Dependent Claim
50 words
The apparatus of claim 13 wherein the noise variance is determined by calculating an energy contained in each predefined block using the following equation: ##EQU11## where M and N are the dimensions of the predefined block and i,j denotes a row and column of a pixel in the predefined block.
Claim 17
Dependent Claim
44 words
The apparatus of claim 13 wherein the local variance of the DFD is determined using the following equation: ##EQU12## where M and N are the dimensions pf the predefined block and i,j denotes a row and column of a pixel in the predefined block.
Claim 18
Dependent Claim
37 words
The apparatus of claim 13 wherein noise visibility function is determined using the following equation: ##EQU13## where σ.sub.ƒ 2 is the local variance, σ n 2 is the noise variance, and Θ is the predetermined image type.
Claim 19
Dependent Claim
50 words
The apparatus of claim 13 wherein the Gaussian kernel is determined using the following equation: ##EQU14## where σ g 2 is the predetermined base variance, Z is the predetermined normalizing coefficient, υ(Θ) is the noise visibility function, and m,n denotes a row and column of a pixel in the DFD.
Claim 20
Dependent Claim
40 words
The apparatus of claim 13 wherein the DFD signal convolved with the Gaussian kernel is determined using the following equation: ##EQU15## where g(m,n) is the Gaussian kernel and m,n denotes a row and column of a pixel in the DFD.
Claim 21
Dependent Claim
14 words
The apparatus of claim 13 wherein the apparatus includes a Digital Signal Processor, DSP.
Claim 22
Dependent Claim
15 words
The apparatus of claim 13 wherein the apparatus includes an Application Specific Integrated Circuit, ASIC.
Claim 23
Dependent Claim
12 words
The apparatus of claim 13 wherein the apparatus includes a gate array.
Claim 24
Dependent Claim
18 words
The apparatus of claim 13 wherein the apparatus includes a computer usable medium having computer readable program codes.
Claim 25
Dependent Claim
14 words
The apparatus of claim 22 wherein the computer usable medium is a computer diskette.
Claim 26
Dependent Claim
17 words
The apparatus of claim 22 wherein the computer usable medium is a memory unit of the computer.
References Cited (Backward Citations)
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2001-06-08 | 2001-12-08 | 2002-06-09 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-06-08 | 2005-12-08 | 2006-06-09 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-06-08 | 2009-12-08 | 2010-06-09 | $7700.00 | Window Closed / Lapsed |