An image processing apparatus decodes encoded RAW data that includes subband data being encoded with lossy encoding scheme, and determines one of a plurality of classifications based on the decoded subband data, wherein the plurality of classifications are based on a feature of an image. The apparatus also obtains correction data corresponding to the determined classification, and corrects recomposed data, which is obtained by applying frequency recomposition to the decoded subband data, based on the correction data, in order to obtain the corrected data as decoded RAW data.

The described technology is generally directed towards developing an adaptive bitrate stack (ladder) on a per-title basis. Variable bitrate encodings are used to obtain complexity information for a title and per-frames scores for the encodings; another encoding provides scene data. The complexity information is analyzed and processed based on the scene data to determine scene-based (e.g., objective and/or subjective quality) scores, which are used to determine scores for the encodings. The results are used to derive a candidate stack, comprising various resolutions and bitrates that provide desirable results. The candidate stack is evaluated by encoding the title using the candidate stack. These encodings are evaluated to select one resolution from any duplicate resolutions for a bitrate (e.g., based on relative quality), resulting in a pruned, final ladder that is associated with the title as the adaptive bitrate stack to be used for streaming that title's content.

A method, computer program, and computer system is provided for splitting viewport bitstreams. A first coded video bitstream is at least partially encoded using a first Group-of-Pictures (GOP) structure at a first resolution. A second coded video bitstream structured into a plurality of tiles is encoded using a second GOP structure at a second resolution, whereby the second GOP structure includes fewer coded pictures than the first GOP structure. A streaming bitstream for decoding or rendering is created using the first coded video bitstream and the second coded bitstream.

A system for controlling power distribution within a vehicular communication network, including a power source equipment comprising a first port in communication with a network node module of a device, and a Power over Ethernet (POE) management module. The POE management module is configured to enable POE to the device via the first port, monitor a current draw of the device, determine whether the current draw of the device exceeds a threshold, and disable POE to the device, responsive to determining that the current draw exceeds the threshold.

A system for controlling power distribution within a vehicular communication network, including a power source equipment comprising a first port in communication with a network node module of a device, and a Power over Ethernet (POE) management module. The POE management module is configured to enable POE to the device via the first port, monitor a current draw of the device, determine whether the current draw of the device exceeds a threshold, and disable POE to the device, responsive to determining that the current draw exceeds the threshold.

Several improvements for use with Bidirectionally Predictive (B) pictures within a video sequence are provided. In certain improvements Direct Mode encoding and/or Motion Vector Prediction are enhanced using spatial prediction techniques. In other improvements Motion Vector prediction includes temporal distance and subblock information, for example, for more accurate prediction. Such improvements and other presented herein significantly improve the performance of any applicable video coding system/logic.

Several improvements for use with Bidirectionally Predictive (B) pictures within a video sequence are provided. In certain improvements Direct Mode encoding and/or Motion Vector Prediction are enhanced using spatial prediction techniques. In other improvements Motion Vector prediction includes temporal distance and subblock information, for example, for more accurate prediction. Such improvements and other presented herein significantly improve the performance of any applicable video coding system/logic.

A coding tool set enablement scheme allows one or more coding tools to be used in the encoding or decoding of video data based on one or more characteristics related to the video. In one embodiment, some coding tools are enabled for block sizes below a particular threshold. In another embodiment, a group of coding tools are enabled based on one flag for blocks having a particular characteristic. In another embodiment, these flags are included in a bitstream with the video. In another embodiment, at least one of the flags are inferred to enable some coding tools.

System and method for improving video encoding and/or video decoding. In embodiments, a video encoding pipeline includes a main encoding pipeline that compresses source image data corresponding with an image frame by processing the source image data based at least in part on encoding parameters to generate encoded image data. Additionally the video encoding pipeline includes a machine learning block communicatively coupled to the main encoding pipeline, in which the machine learning block analyzes content of the image frame by processing the source image data based at least in part on machine learning parameters implemented in the machine learning block when the machine learning block is enabled by the encoding parameters; and the video encoding pipeline adaptively adjusts the encoding parameters based at least in part on the content expected to be present in the image frame to facilitate improving encoding efficiency.

System and method for improving video encoding and/or video decoding. In embodiments, a video encoding pipeline includes a main encoding pipeline that compresses source image data corresponding with an image frame by processing the source image data based at least in part on encoding parameters to generate encoded image data. Additionally the video encoding pipeline includes a machine learning block communicatively coupled to the main encoding pipeline, in which the machine learning block analyzes content of the image frame by processing the source image data based at least in part on machine learning parameters implemented in the machine learning block when the machine learning block is enabled by the encoding parameters; and the video encoding pipeline adaptively adjusts the encoding parameters based at least in part on the content expected to be present in the image frame to facilitate improving encoding efficiency.