Method and apparatus for measuring the quality of video data

• US 7,038,710 B2
• Filed: 07/17/2002
• Issued: 05/02/2006
• Est. Priority Date: 07/17/2002
• Status: Expired due to Fees

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Caviedes, Jorge E.
• Primary Examiner(s): Miller, John
• Assistant Examiner(s): DESIR, JEAN WICEL
• Application Number:
  US10/197,334
• Publication Number:
  US 20040012675A1
• Time in Patent Office: 1,385 Days
• Field of Search: 348/180, 348/185, 348/189, 348/191, 348/192, 348/193, 382/167, 382/255, 382/272, 382/274, 382/275, 455/226.1, 455/226.2, 455/226.3, 455/115.1, 455/423
• US Class Current: 348/180
• CPC Class Codes: H04N 17/004 for digital television systems
• 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 19/527 Global motion vector estimation

Abstract

The present invention relates to a method and apparatus for generating a model used to estimate the quality of input-video data. The apparatus is configured to obtain a plurality of average subjective scores from a plurality of sample video data, and a plurality of image metrics for each of the plurality of said average subjective scores is calculated. Some of the plurality of said image metrics is converted into corresponding, perceived quality metrics, then an image-quality metric is predicted based on the calculated quality metrics and the perceived quality metrics.

Claims

1. A method for generating a subjective reference model used to estimate the quality of input-video data, the method comprising the steps of:

obtaining a plurality of average subjective scores from a plurality of sample video data comprising the steps of

providing the plurality of said sample video data to a plurality of viewers under a predetermined condition;

converting each of the plurality of said sample video data into a plurality of processed video data according to predetermined criteria; and

,

obtaining said average subjective scores based on the plurality of said sample video data and the corresponding said processed video data;

calculating a plurality of image metrics for each of the plurality of said average subjective scores;

converting at least some of the plurality of said image metrics into corresponding, perceived quality metrics; and

,

deriving said model based on the plurality of said image metrics and said perceived quality metrics.

2. The method of claim 1, further comprising the step of applying said subjective reference model to said input-video data to produce an objective quality metric.

3. The method of claim 1, wherein the converting step further comprises the step of modifying sharpness, compression, noise, and blocking of the plurality of said sample video data or in any combination thereof.

4. The method of claim 1, wherein said predetermined condition complies with an International Telecommunication Union (ITU) Recommendation 500.

5. The method of claim 3, wherein the plurality of said sample video data is representative of a wide range of video sequences typically viewed by an average consumer.

6. The method of claim 1, wherein the plurality of said image metrics comprise at least one of blocking, ringing, clipping, noise, contrast, and sharpness metrics.

7. The method of claim 1, wherein said perceived quality metrics comprise at least one of blocking, ringing, clipping, and sharpness perceived metrics.

8. The method of claim 1, wherein the step of deriving said subjective reference model further comprising the step of computing a combined perceived effect between said calculated image metrics and said perceived image metrics.

9. The method of claim 8, wherein the step of computing said combined perceived effect comprises the step of calculating an interaction between a contrast metric, a perceived sharpness metric, and a perceived blocking metric measured from at least some of the plurality of said image metrics.

10. The method of claim 8, wherein the step of computing said combined perceived effect includes the step of calculating an interaction between a perceived ringing metric and a perceived blocking metric measured from at least some of the plurality of said image metrics.

11. The method of claim 8, wherein the step of computing said combined perceived effect includes the step of calculating an interaction between a contrast metric and a perceived clipping metric measured from at least some of the plurality of said image metrics.

12. The method of claim 8, wherein the step of computing said combined perceived effect includes the step of calculating an interaction between a perceived sharpness metric, a contrast metric, and a noise metric measured from at least some of the plurality of said image metrics.

13. The method of claim 8, wherein the step of computing said combined perceived effect includes the step of calculating an interaction between a perceived clipping metric, a contrast metric, and a noise metric measured from at least some of the plurality of said image metrics.

14. An apparatus for generating a subjective reference model used to estimate the quality of input video data, comprising:

a processor;

a memory, coupled to said processor, said memory being configured to allow the processor to;

obtain a plurality of average subjective scores from a plurality of sample video data by;

providing the plurality of said sample video data to a plurality of viewers under a predetermined condition;

converting each of the plurality of said sample video data into a plurality of processed video data according to predetermined criteria; and

,

obtaining said average subjective scores based on the plurality of said sample video data and the corresponding said processed video data calculate a plurality of image metrics for each of the plurality of said average subjective scores;

convert at least some of the plurality of said image metrics into corresponding perceived quality metrics; and

,

derive said subjective reference model based on the plurality of said image metrics and said perceived quality metrics.

15. The apparatus of claim 14, wherein said memory is further configured to allow said processor to apply said subjective reference model to said input-video data to produce an objective quality metric.

16. The apparatus of claim 14, wherein to convert each of the plurality of said sample video data into the plurality of said processed video data according to said predetermined criteria, said memory is further configured to allow said processor to modify the sharpness, compression, noise, and blocking of the plurality of said sample video data or in any combination thereof.

17. The apparatus of claim 14, wherein the plurality of said image metrics comprise at least one of blocking, ringing, clipping, noise, contrast, and sharpness metrics.

18. The apparatus of claim 14, wherein said perceived quality metrics comprise at least one of blocking, ringing, clipping, noise, contrast, and sharpness-perceived metrics.

19. The apparatus of claim 14, wherein said memory is further configured to allow said processor to compute a combined perceived effect between said calculated image metrics and said perceived image metrics.

20. The apparatus of claim 14, wherein said memory is further configured to allow said processor to calculate an interaction among a contrast metric, a perceived sharpness metric, and a perceived blocking metric measured from at least some of the plurality of said image metrics.

21. The apparatus of claim 19, wherein to compute said combined perceived effect, said memory is further configured to allow said processor to calculate an interaction between a perceived ringing metric and a perceived blocking metric measured from at least some of the plurality of said image metrics.

22. The apparatus of claim 19, wherein to compute said combined perceived effect, said memory is further configured to allow said processor for an interaction between a contrast metric and a perceived clipping metric measured from at least some of the plurality of said image metrics.

23. The apparatus of claim 19, wherein to compute said combined perceived effect, said memory is further configured to allow said processor to calculate an interaction between a perceived sharpness metric, a contrast metric, and a noise metric measured from at least some of the plurality of said image metrics.

24. The apparatus of claim 19, wherein to compute said combined perceived effect, said memory is further configured to allow said processor to calculate an interaction between a perceived clipping metric, a contrast metric, and a noise metric measured from at least some of the plurality of said image metrics.