METHOD AND APPARATUS FOR INTRA-PREDICTION IN A VIDEO ENCODER

• US 20100246675A1
• Filed: 03/30/2009
• Published: 09/30/2010
• Est. Priority Date: 03/30/2009
• Status: Abandoned Application

• Current Assignee: Sony Corporation (Sony Group Corp.), Sony Electronics Inc. (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.), Sony Electronics Inc. (Sony Group Corp.)
• Inventors: Sodagar, Iraj, Gharavi-Alkhansari, Mohammad, Tabatabai, Ali, Visharam, Mohammed Zubair
• Application Number:
  US12/414,256
• Publication Number:
  US 20100246675A1
• Time in Patent Office: Days
• US Class Current: 375/240.130
• CPC Class Codes: H04N 19/105 Selection of the reference unit for prediction within a chosen coding or prediction mode, e.g. adaptive choice of position and number of pixels used for prediction
• CPC Class Codes: H04N 19/11 among a plurality of spatial predictive coding modes
• CPC Class Codes: H04N 19/134 characterised by the element, parameter or criterion affecting or controlling the adaptive coding
• CPC Class Codes: H04N 19/176 the region being a block, e.g. a macroblock
• CPC Class Codes: H04N 19/593 involving spatial prediction techniques
• CPC Class Codes: H04N 19/61 in combination with predictive coding
• CPC Class Codes: H04N 19/97 Matching pursuit coding

Abstract

Method and apparatus for intra-prediction in a video encoder are described. An aspect relates to a method of intra-prediction for a group of samples in an image being coded. In some examples, the method includes: defining a target template for the group of samples; comparing the target template with affine transformations of candidate templates within a search area of the image; identifying at least one matching template of the candidate templates as matching the target template; determining a candidate group of samples based on the at least one matching template; and coding the group of samples using the candidate group of samples as a predictor.

Claims

1. A method of intra-prediction for a group of samples in an image being coded, comprising:

defining a target template for the group of samples;

comparing the target template with affine transformations of candidate templates within a search area of the image;

identifying at least one matching template of the candidate templates as matching the target template;

determining a candidate group of samples based on the at least one matching template; and

coding the group of samples using the candidate group of samples as a predictor.

2. The method of claim 1, wherein the at least one matching template is a best matching template, and wherein the steps of comparing and identifying comprise:

computing, for each candidate template of the candidate templates, an optimal affine transformation of the candidate template that minimizes a mean squared error (MSE) with respect to the target template; and

identifying, as the best matching template, a respective one of the candidate templates the optimal affine transformation of which results in a smallest MSE.

3. The method of claim 2, wherein the step of determining comprises:

applying coefficients of the optimal affine transformation of the best matching template to a group of samples associated with the best matching template to produce the candidate group of samples.

4. The method of claim 1, wherein the at least one matching template is M best matching templates, where M greater than one and less than a number of samples in the target template, and wherein the steps of comparing and identifying comprise:

computing, for each candidate template of the candidate templates, an optimal affine transformation of the candidate template that minimizes a mean squared error (MSE) with respect to the target template; and

identifying, as the M best matching templates, respective ones of the candidate templates the optimal affine transformations of which result in the M smallest mean squared errors.

5. The method of claim 4, wherein the step of determining comprises:

computing a linear projection of the target template onto a subspace defined by the M best matching templates;

computing a weighted average of groups of samples associated with the M best matching templates using coefficients of the linear projection as respective weights;

at least one of rounding or clipping samples of the weighted average to produce the candidate group of samples.

6. The method of claim 1, wherein the at least one matching template is M matching templates, where M greater than one and less than a number of samples in the target template, and wherein the steps of comparing and identifying comprise:

computing, for each candidate template of the candidate templates, an optimal affine transformation of the candidate template that minimizes a mean squared error (MSE) with respect to the target template; and

identifying, as the M matching templates, respective ones of the candidate templates the optimal affine transformations of which result in mean squared errors less than a threshold error.

7. The method of claim 6, wherein the M matching templates are identified using matching pursuit.

8. The method of claim 6, wherein the step of determining comprises:

computing a linear projection of the target template onto a subspace defined by the M matching templates;

computing a weighted average of groups of samples associated with the M matching templates using coefficients of the linear projection as respective weights;

at least one of rounding or clipping samples of the weighted average to produce the candidate group of samples.

9. The method of claim 1, wherein the candidate templates are selected within the search area with sub-pixel accuracy.

10. The method of claim 1, further comprising:

selecting a strength of a low pass filter based on a function of error between the at least one matching template and the target template; and

filtering the candidate group of samples using a low pass filter having the strength as selected.

11. Apparatus for intra-prediction for a group of samples in an image being coded, comprising:

an encoder configured to code the group of samples using a candidate group of samples as a predictor;

a temporal/spatial prediction module, within the encoder, configured to;

define a target template for the group of samples;

compare the target template with affine transformations of candidate templates within a search area of the image;

identify at least one matching template of the candidate templates as matching the target template;

determine the candidate group of samples based on the at least one matching template.

12. The apparatus of claim 11, wherein the at least one matching template is a best matching template, and wherein the temporal/spatial prediction module is configured to:

compute, for each candidate template of the candidate templates, an optimal affine transformation of the candidate template that minimizes a mean squared error (MSE) with respect to the target template; and

identify, as the best matching template, a respective one of the candidate templates the optimal affine transformation of which results in a smallest MSE.

13. The apparatus of claim 12, wherein the temporal/spatial prediction module is further configured to:

apply coefficients of the optimal affine transformation of the best matching template to a group of samples associated with the best matching template to produce the candidate group of samples.

14. The apparatus of claim 11, wherein the at least one matching template is M best matching templates, where M greater than one and less than a number of samples in the target template, and wherein the temporal/spatial prediction module is configured to:

compute, for each candidate template of the candidate templates, an optimal affine transformation of the candidate template that minimizes a mean squared error (MSE) with respect to the target template; and

identify, as the M best matching templates, respective ones of the candidate templates the optimal affine transformations of which result in the M smallest mean squared errors.

15. The apparatus of claim 14, wherein the temporal/spatial prediction module is configured to:

compute a linear projection of the target template onto a subspace defined by the M best matching templates;

compute a weighted average of groups of samples associated with the M best matching templates using coefficients of the linear projection as respective weights;

at least one of round or clip samples of the weighted average to produce the candidate group of samples.

16. The apparatus of claim 11, wherein the at least one matching template is M matching templates, where M greater than one and less than a number of samples in the target template, and wherein the temporal/spatial prediction module is configured to:

compute, for each candidate template of the candidate templates, an optimal affine transformation of the candidate template that minimizes a mean squared error (MSE) with respect to the target template; and

identify, as the M matching templates, respective ones of the candidate templates the optimal affine transformations of which result in mean squared errors less than a threshold error.

17. The apparatus of claim 16, wherein the temporal/spatial prediction module is configured to:

compute a linear projection of the target template onto a subspace defined by the M matching templates;

compute a weighted average of groups of samples associated with the M matching templates using coefficients of the linear projection as respective weights;

at least one of round or clip samples of the weighted average to produce the candidate group of samples.

18. The apparatus of claim 11, wherein the temporal/spatial prediction module selects the candidate templates within the search area with sub-pixel accuracy.

19. The apparatus of claim 11, wherein the temporal/spatial prediction module is configured to:

select a strength of a low pass filter based on a function of error between the at least one matching template and the target template; and

filter the candidate group of samples using a low pass filter having the strength as selected.

20. Apparatus for intra-prediction for a group of samples in an image being coded, comprising:

means for defining a target template for the group of samples;

means for comparing the target template with affine transformations of candidate templates within a search area of the image;

means for identifying at least one matching template of the candidate templates as matching the target template;

means for determining a candidate group of samples based on the at least one matching template; and

means for coding the group of samples using the candidate group of samples as a predictor.