Time compressing a sequence of frames of content
Methods and systems for compressing video content are presented. The methods and systems include analyzing a sequence of media frames stored in the memory device and calculating a displacement level of each of the media frames. The displacement level indicates how different each of the media frames is to a previous media frame. The sequence of media frames is divided into a plurality of cuts where each cut ends at a media frame having a substantially high displacement level. Frames to be removed from the sequence of media frames are identified in each cut based upon the frame's displacement level. The identified frames are then removed.
1. A method comprising:
receiving a sequence of frames of content associated with a first time duration;
removing one or more frames, from the sequence of frames of content, that were added based on a pulldown pattern, to compress the sequence of frames of content; and
causing output of the compressed sequence of frames of content during a second time duration, wherein the first time duration is longer than the second time duration.
2. The method of claim 1 , wherein the compressed sequence of frames of content comprises advertising content.
3. The method of claim 1 , further comprising:
determining one or more frames of the sequence of frames of content that were added based on the pulldown pattern.
4. The method of claim 1 , further comprising:
determining a displacement value for each frame of the sequence of frames of content.
5. The method of claim 4 , wherein the displacement value indicates a difference in color or position between each frame of the of frames of content and one or more previous frames in the sequence of frames of content.
6. The method of claim 1 , further comprising:
dividing, based on one or more displacement values in the sequence of frames of content exceeding a threshold, the sequence of frames of content into a plurality of scenes.
7. The method of claim 6 , wherein the removing comprises:
dividing the plurality of scenes such that each scene of the plurality of scenes has at most one pulldown pattern.
8. The method of claim 1 , wherein the removing is based on at least one of: a neural network, a support vector machine, or a decision tree.
9. A device comprising:
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the device to:
receive a sequence of frames of content associated with a first time duration;
remove one or more frames, from the sequence of frames of content, that were added based on a pulldown pattern, to compress the sequence of frames of content; and
cause output of the compressed sequence of frames of content during a second time duration, wherein the first time duration is longer than the second time duration.
10. The device of claim 9 , wherein the compressed sequence of frames of content comprises advertising content.
11. The device of claim 9 , wherein the instructions, when executed by the one or more processors, further cause the device to:
determine one or more frames of the sequence of frames of content that were added based on the pulldown pattern.
12. The device of claim 9 , wherein the instructions, when executed by the one or more processors, further cause the device to:
determine a displacement value for each frame of the sequence of frames of content.
13. The device of claim 12 , wherein the displacement value indicates a difference in color or position between each frame of the of frames of content and one or more previous frames in the sequence of frames of content.
14. The device of claim 9 , wherein the instructions, when executed by the one or more processors, further cause the device to:
divide, based on one or more displacement values in the sequence of frames of content exceeding a threshold, the sequence of frames of content into a plurality of scenes.
15. The device of claim 14 , wherein the removing comprises:
dividing the plurality of scenes such that each scene of the plurality of scenes has at most one pulldown pattern.
16. The device of claim 9 , wherein the removing is based on at least one of: a neural network, a support vector machine, or a decision tree.
17. A non-transitory computer-readable medium storing instructions that, when executed, cause:
receiving a sequence of frames of content associated with a first time duration;
removing one or more frames, from the sequence of frames of content, that were added based on a pulldown pattern, to compress the sequence of frames of content; and
causing output of the compressed sequence of frames of content during a second time duration, wherein the first time duration is longer than the second time duration.
18. The non-transitory computer-readable medium of claim 17 , wherein the compressed sequence of frames of content comprises advertising content.
19. The non-transitory computer-readable medium of claim 17 , wherein the instructions, when executed, further cause:
determining one or more frames of the sequence of frames of content that were added based on the pulldown pattern.
20. The non-transitory computer-readable medium of claim 17 , wherein the instructions, when executed, further cause:
determining a displacement value for each frame of the sequence of frames of content, wherein the displacement value indicates a difference in color or position between each frame of the of frames of content and one or more previous frames in the sequence of frames of content.