Detecting progressive video

• US 20070002169A1
• Filed: 06/29/2005
• Published: 01/04/2007
• Est. Priority Date: 06/29/2005
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Spears, Stacey, Munsil, Donald
• Granted Patent: US 7,561,206 B2
• Time in Patent Office: Days
• US Class Current: 348/446
• CPC Class Codes: H04N 5/147 Scene change detection
• CPC Class Codes: H04N 7/0115 with details on the detection of a particular field or frame pattern in the incoming video signal, e.g. 3:2 pull-down pattern
• CPC Class Codes: H04N 7/012 Conversion between an interlaced and a progressive signal for graphics images G09G2310/0229

Abstract

Whether interlaced video fields form a progressive video frame can be automatically determined. The presence or absence of a first characteristic of one or more video fields can be determined by analysis of the fields and/or related information such as flags, cadence, previous determinations, and others. Similarly, the presence or absence of a second characteristic can be detected. In accordance with the detecting, how likely or whether the two or more video fields form a progressive video frame can be determined based on a possibly predetermined likelihood that fields of progressive video frames in general have or do not have the first characteristic and based on a possibly predetermined likelihood that fields of interlaced video frames in general have or do not have the second characteristic.

Claims

1. A method of determining whether video fields form a progressive video frame, the method comprising:

obtaining a plurality of probability observations, each observation being obtained using a different basis of analysis of the video fields; and

combining the probability observations to determine whether the fields form a progressive video frame.

2. A method according to claim 1, wherein the combining comprises a Bayesian calculation using the probability observations.

3. A method according to claim 2, wherein the Bayesian calculation comprises a Bayesian inference network or a naive Bayesian calculation.

4. A method according to claim 1, wherein one of the observations comprises comparing the two fields using a sum of absolute differences of a luma channel and assigning a set of probability estimates based on whether a result of the comparison is above or below a constant.

5. A method according to claim 4, wherein the sum of absolute differences is mapped to one of a plurality of ranges of possible values and a set of probability estimates is assigned based on which range the sum of absolute differences is mapped to, or wherein the sum of absolute differences is supplied to a set of polynomial equations that produces as output the set of probability estimates.

6. A method according to claim 1, wherein one of the observations comprises a comparison of the two fields using a sum of squared differences of a luma channel and assigning a set of probability estimates based on whether a result of the comparison is above or below a threshold.

7. A method according to claim 1, wherein the first basis of analysis comprises either hysterisis, or combing detection, or flag analysis, or cadence analysis, or cadence pattern frequency analysis.

8. A method according to claim 7, wherein the second basis of analysis comprises either hysterisis, or combing detection, or flag analysis, or cadence analysis, or cadence pattern frequency analysis, but is a different basis of analysis than the first basis of analysis.

9. A method according to claim 1, wherein either the first or the second basis of analysis comprises either analysis of a set of compression flags associated with previous and next fields in a sequence of frames, or analysis of a number of previous fields that have been previously found to be part of a progressive frame.

10. A method according to claim 1, wherein the first basis of analysis comprises analyzing video content of fields and the second basis of analysis comprises analyzing flags of frames.

11. A machine-readable medium storing information for performing a process, the process comprising:

automatically detecting the presence or absence of a first characteristic of one or more video fields;

automatically detecting the presence or absence of a second characteristic of the one or more video fields; and

in accordance with the detecting, determining how likely or whether the two or more video fields form a progressive video frame based on a likelihood that fields of progressive or interlaced video frames in general have or do not have the first characteristic and based on a likelihood that fields of progressive or interlaced video frames in general have or do not have the second characteristic.

12. A machine-readable medium according to claim 11, wherein the determination comprises a Bayesian inference using the likelihoods.

13. A machine-readable medium according to claim 12, further comprising comparing a likelihood of whether the video frame is a progressive video frame to a threshold.

14. A machine-readable medium according to claim 13, further comprising using the one or more fields to deinterlace the video frame when the threshold exceeds the likelihood.

15. A device for determining whether video fields form a progressive frame, where the device is configured to be capable of performing a process, the process comprising:

automatically analyzing the video fields and performing a Bayesian calculation based on the analyzing to determine whether the video fields form a progressive frame.

16. A device according to claim 15, wherein the analyzing comprises performing a first type of automatic analysis of the video fields, performing a second type of automatic analysis of the video fields, and wherein the Bayesian calculation is based on outputs of both analyses to determine whether the video fields form a progressive video frame.

17. A device according to claim 16, further comprising first analysis means for performing the first type of automatic analysis, and second analysis means for performing the second type of automatic analysis.

18. A device according to claim 16, wherein one of the observations is obtained by comparing the video fields using a sum of absolute differences of a luma channel.

19. A device according to claim 18, wherein the process further comprises assigning a set of probability estimates based on whether the sum of absolute differences is above or below a given threshold.

20. A device according to claim 15, further comprising a deinterlacing unit deinterlacing the video fields according to the determination of whether the video fields form a progressive frame.