Abstract: Methods and devices are provided for encoding a video stream which comprise encoding a plurality of frames of video acquired from different points of view, generating statistical values for the frames of video determined from values of pixels of the frames, generating, for each of the plurality of frames, a perceptual hash value based on statistical values of the frame and encoding a current frame comprising video acquired from a corresponding one of the different points of view using a previously encoded reference frame based on a similarity of perceptual hashes of the current frame and the previously encoded reference frame.

Abstract: Systems, apparatuses, and methods for performing machine learning content categorization leveraging video encoding pre-processing are disclosed. A system includes at least a motion vector unit and a machine learning (ML) engine. The motion vector unit pre-processes a frame to determine if there is temporal locality with previous frames. If the objects of the scene have not changed by a threshold amount, then the ML engine does not process the frame, saving computational resources that would typically be used. Otherwise, if there is a change of scene or other significant changes, then the ML engine is activated to process the frame. The ML engine can then generate a QP map and/or perform content categorization analysis on this frame and a subset of the other frames of the video sequence.

Abstract: Methods and devices are provided for encoding a video stream which comprise encoding a plurality of frames of video acquired from different points of view, generating statistical values for the frames of video determined from values of pixels of the frames, generating, for each of the plurality of frames, a perceptual hash value based on statistical values of the frame and encoding a current frame comprising video acquired from a corresponding one of the different points of view using a previously encoded reference frame based on a similarity of perceptual hashes of the current frame and the previously encoded reference frame.

Abstract: Systems, apparatuses, and methods for performing machine learning content categorization leveraging video encoding pre-processing are disclosed. A system includes at least a motion vector unit and a machine learning (ML) engine. The motion vector unit pre-processes a frame to determine if there is temporal locality with previous frames. If the objects of the scene have not changed by a threshold amount, then the ML engine does not process the frame, saving computational resources that would typically be used. Otherwise, if there is a change of scene or other significant changes, then the ML engine is activated to process the frame. The ML engine can then generate a QP map and/or perform content categorization analysis on this frame and a subset of the other frames of the video sequence.