Scalable expandable system and method for optimizing a random system of algorithms for image quality

• US 20030026497A1
• Filed: 07/25/2001
• Published: 02/06/2003
• Est. Priority Date: 07/25/2001
• Status: Active Grant

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Ali, Walid S. I.
• Granted Patent: US 6,813,390 B2
• Time in Patent Office: Days
• US Class Current: 382/278
• CPC Class Codes: H04N 19/127 Prioritisation of hardware or computational resources
• CPC Class Codes: H04N 19/14 Coding unit complexity, e.g. amount of activity or edge presence estimation H04N19/146 takes precedence
• CPC Class Codes: H04N 19/156 Availability of hardware or computational resources, e.g. encoding based on power-saving criteria
• CPC Class Codes: H04N 19/189 characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding
• CPC Class Codes: H04N 19/196 being specially adapted for the computation of encoding parameters, e.g. by averaging previously computed encoding parameters processing of motion vectors H04N19/513

Abstract

An optimizing video processing method and system selects algorithms for best obtainable video quality for the available computation resources. A video processing module, which processes an input of a video stream, architectural parameters for identifying an order of cascaded video functions and determining a bit precision between data of any consecutive cascaded functions according to an associated complexity level which correlates with a value of available computational resources. An optimizer module optimizes processing of the video stream and includes a plurality of optimization engines each having an associated complexity level. The optimizer module selects an optimization engine according a complexity level which correlates with the value of available computational resources. An Object Image Quality (OIQ) evaluator module evaluates an image quality of an output of the video stream from the video processing module. The OIQ evaluator module includes a plurality of objective image quality metrics having an associated complexity level. The OIQ evaluator module selects a metric according to a correlation factor and a complexity level for said value of available computation resources.

Claims

1. An optimizing video processing system comprising:

a video processing module for processing an input of a video stream, said video processing module comprising architectural parameters for identifying an order of cascaded video functions and determining a bit precision between data of any consecutive cascaded functions according to an associated complexity level which correlates with a value of available computational resources;

an optimizer module for optimizing processing of said video stream, said optimizer module being in communication with said video processing module, said optimizer module comprising a plurality of optimization engines each having an associated complexity level, said optimizer module includes means for selecting an optimization engine according to a complexity level which correlates with said value of available computational resources; and

an Object Image Quality (OIQ) evaluator module for evaluating an image quality of an output of said video stream from said video processing module, said OIQ evaluator comprising a plurality of objective image quality metrics having an associated complexity level, and said OIQ evaluator module includes means for selecting a metric from said plurality of objective metrics according to a correlation factor R and a complexity level for said value of available computation resources.

2. The system according to claim 1, wherein said means for selecting the metric by said OIQ evaluator module includes determining said correlation factor R determined according to the following equation:

3. The optimizing video processing system according to claim 1, further comprising:

a computational resource analyzer for selecting the associated complexity level of at least one of said video processing module, said optimizer module, and said OIQ evaluator module.

4. The system according to claim 1, wherein said optimizer module includes both deterministic and non-deterministic optimization engines.

5. The system according to claim 4, wherein said optimizer module includes heuristic search engines comprising at least one of genetic algorithms (GA), simulated annealing (SA), tabu search (TS), simulated evolution (SE), and stochastic evolution.

6. The system according to claim 2 wherein said optimizer module includes heuristic search engines comprising at least one of genetic algorithms (GA), simulated annealing (SA), tabu search (TS), simulated evolution (SE) and stochastic evolution.

7. The video processing system according to claim 1, wherein said optimizer module is scalable.

8. The video processing system according to claim 2, wherein said optimizer module is scalable.

9. The video processing system according to claim 1, wherein said OIQ evaluator module is scalable.

10. The video processing system according to claim 2, wherein said OIQ evaluator module is scalable.

11. The video processing system according to claim 7, wherein said OIQ evaluator module is scalable.

12. The video processing system according to claim 2, wherein said computational resource analyzer module selects the level of complexity for the at least one of said video processing module, said optimizer module, and said OIQ evaluator module by detecting available computational resources for the at least one of said video processing module, said optimizer module and said OIQ evaluator module.

13. A method for optimizing video algorithms for available computation resources, said method comprising:

(a) identifying an order of cascaded video functions by a video processing module for processing of a video stream input to said video processing module according to an associated level of complexity which correlates with a value of available computational resources;

(b) selecting an optimization method for optimizing the processing of the video stream, said optimization method being selected from a plurality of optimization methods according to an associated complexity level which correlates with said value of available computational resources;

(c) evaluating an objective image quality of the video stream after the video stream is output from said video processing module;

wherein the evaluating of said objective image quality of the video stream is determined by selecting a metric from a plurality of metrics according to a correlation factor R and an associated complexity level for said value of computational resources.

14. The method according to claim 13, wherein the evaluating of said objective image quality in step (c) includes

determining said correlation factor R according to the following equation:

$F=\underset{R}{\max }<br><br>\left\{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>w_i<br><br>f_i\right\},$

wherein F is a final metric of the quality of the video as judged by the system, F being determined by finding a set of weights w_i, which when multiplied by each individual metric f_i of said plurality of metrics (which ranges from 1 to n) maximizes the correlation factor R with a predetermined subjective evaluation.

15. The method according to claim 14, further comprising

(d) selecting the associated complexity level of at least one of step (a), (b) and (c) by a computational resource analyzer.

16. The method according to claim 13, wherein the plurality of optimization methods selected in step (b) includes both deterministic and non-deterministic optimization methods.

17. The method according to claim 16, wherein the plurality of optimization methods include heuristic search engines comprising at least one of genetic algorithms (GA), simulated annealing (SA), tabu search (TS), simulated evolution (SE), and stochastic evolution.

18. The method according to claim 15, wherein said the associated complexity level selected in step (d) includes detecting computational resources available for at least one of steps (a), (b) and (c).

19. The method according to claim 13, wherein the video processing module recited in step (a) is scalable.

20. The method according to claim 13, wherein step (b) includes providing a scalable optimizer for selecting the optimization method.

21. The method according to claim 13, wherein step (c) includes providing a scalable objective image quality evaluator for evaluating the objective image quality.