High dynamic range video format detection

1. A video format detector, comprising:

a video input structured to receive a video signal in a one of a plurality of possible video formats;

a cumulative distribution function generator structured to generate a cumulative distribution function curve from a component of the video signal;

a feature detector structured to generate one or more feature vectors from the cumulative distribution function curve;

a video classifier structured to accept the one or more feature vectors from the feature detector and to generate a prediction of the video format in which the video signal was produced based, at least in part, on the received feature vectors;

a recursive filter configured to filter the prediction of the video format from the video classifier and output a filtered prediction of the video format; and

a threshold detector configured to receive the filtered prediction of the video format and output a detected video format based on whether the filtered prediction of the video format violates one or more thresholds.

2. The video format detector according to claim 1 further comprising a multiplexer configured to receive the detected video format from the threshold detector and select an input of the multiplexer based on the detected video format.

3. The video format detector according to claim 2 , further comprising a converter configured to receive the prediction of the video format from the video classifier and convert the component of the signal to a converted component of the signal, wherein the multiplexer includes a first input to receive the component of the signal and a second input to receive the converted component of the signal.

4. The video format detector according to claim 3 , wherein the component of the signal is a luminance component of the video signal, a color space component of the video signal, or both a luminance component of the video signal and a color space component of the video signal.

5. The video format detector according to claim 1 , wherein the video classifier is a Bayesian device, a decision tree, or a convolutional neural network.

6. The video format detector according to claim 1 , further comprising a color space classifier structured to accept the one or more feature vectors from the feature detector and to generate a prediction of the color space in which the video signal was produced based, at least in part, on the received feature vectors.

7. A method for detecting a video format of an input video, comprising:

receiving a video signal in one of a plurality of possible video formats;

generating a cumulative distribution function curve from a component of the video signal;

generating one or more feature vectors from the cumulative distribution function curve;

generating, by a video classifier, a prediction of the video format in which the video signal was produced based, at least in part, on the one or more feature vectors;

filtering the prediction of the video format from the video classifier and output a filtered prediction of the video format;

determining whether the filtered prediction of the video format violates a threshold; and

outputting a detected video format based on whether the filtered prediction of the video format violates the threshold.

8. The method according to claim 7 further comprising receiving the detected video format from the threshold detector at a multiplexer and selecting an input of the multiplexer based on the detected video format.

9. The method according to claim 8 , further comprising converting the component of the signal to a converted component of the signal, wherein the multiplexer includes a first input to receive the component of the signal and a second input to receive the converted component of the signal.

10. The method according to claim 9 , wherein the component of the signal is a luminance component of the video signal, a color space component of the video signal, or both a luminance component of the video signal and a color space component of the video signal.

11. The method according to claim 7 , wherein the video classifier is a support vector machine, a Bayesian device, a decision tree, or a convolutional neural network.

12. The method according to claim 7 , further comprising generating a prediction of the color space in which the video signal was produced based, at least in part, on the one or more feature vectors.

13. One or more computer-readable storage media comprising instructions, which, when executed by one or more processors of a video format detector, cause the video format detector to:

generate a cumulative distribution function curve from a component of a received video signal in one of a plurality of possible video formats;

produce one or more feature vectors from the cumulative distribution function curve;

determine, by a video classifier, a prediction of the video format in which the video signal was produced based, at least in part, on the one or more feature vectors;

filter the prediction of the video format from the video classifier and output a filtered prediction of the video format;

determine whether the filtered prediction of the video format violates a threshold; and

output a value indicating the video format based on whether the filtered prediction of the video format violates the threshold.

14. The one or more computer-readable storage media according to claim 13 , further comprising instructions to cause the video format detector to generate a prediction of the color space in which the video signal was produced based, at least in part, on the one or more feature vectors.