Abstract: Approaches in accordance with various illustrative embodiments provide for the determination and/or optimization the quality of an image or video, such as an image that has been compressed for transmission or storage then decompressed for presentation. Weights can be determined for a set of weight-based quality metrics to produce an overall quality metric that is a weighted combination of these metrics. Because different portions of an image or video frame may have different types of features, an image or video frame can be divided into blocks of pixels, for example, with different weights being assigned to different blocks using quality metrics. Different metrics can be considered as points in a high-dimensional weight space, with each dimension corresponding to a weight of a block. A combination of these points results in an improved quality metric. Compression settings can be updated based in part upon the overall quality metric values.

Abstract: A system includes a processing device to receive a video content, a quality metric, and a target bit rate for encoding the video content. The system includes encoding hardware to perform frame encoding on the video content and a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the quality metric and the target bit rate. The controller is further programmed to provide the first QP values to the encoding hardware for rate control of the frame encoding.

Abstract: A system includes a processing device to receive video content and output encoded video of the video content for a client video device. The system includes a controller coupled to the processing device, the controller programmed with machine instructions to receive, from a video encoder while encoding the video content, frame statistics based on one or more encoded frames of the video content corresponding to a current frame. The machine instructions further generate a first quantization parameter (QP) value for the current frame using a frame machine learning model, wherein the frame machine learning model includes states that depend on the frame statistics. The machine instructions further provide the first QP value to the video encoder for rate control of the frame encoding of the current frame.

Abstract: A system includes a processing device to receive video content, metadata related to the video content, and a target bit rate for encoding the video content. The processing device further detects a content type of the video content based on the metadata and encodes hardware to perform frame encoding on the video content. The system further includes a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the content type and the target bit rate. The controller further provides the first QP values to the encoding hardware for rate control of the frame encoding.

Abstract: In one embodiment, a system includes a hardware accelerator to receive video data of multiple video frames, divide each of the video frames into respective blocks, compute encoding assist data including at least one video encoding parameter type for each of the respective blocks of each of the video frames, and store respective portions of the encoding assist data across respective database tables, and an interface to provide the respective database tables to video encoding software running on a processor.

Abstract: A method and system for determining occupancy in a space, include determining presence of an occupant in a space based a signal from a PIR sensor monitoring the space and on analysis of an image of the space. Assigning different weights to the PIR signal and image analysis enables controlling a device in the space differently.

Abstract: A system and method are provided for determining a body position of an occupant form an image, based on the shape of the occupant and based on a visual surrounding of the shape of the occupant in the image.

Abstract: A method and system for determining occupancy in a space, include determining presence of an occupant in a space based a signal from a PIR sensor monitoring the space and on analysis of an image of the space. Assigning different weights to the PIR signal and image analysis enables controlling a device in the space differently.

Abstract: A method for removing blocking artifacts from moving and still pictures, comprising classifying horizontal and vertical boundaries in each picture as blocky or non-blocky; for each blocky boundary, defining an adaptive, picture contentdependent, one-dimensional filtered pixels region of interest (ROI) that crosses the boundary and is bound at each of its ends by a bounding pixel; defining a finite filter having a length correlated with the length of the ROI; defining a filtering pixels expansion that uniquely determines the padding values of the finite length filtered pixels ROI for the finite length filtering; and filtering the ROI pixels using the finite filter and the filtering expansion.

Abstract: A method for removing blocking artifacts from moving and still pictures, comprising classifying horizontal and vertical boundaries in each picture as blocky or non-blocky; for each blocky boundary, defining an adaptive, picture contentdependent, one-dimensional filtered pixels region of interest (ROI) that crosses the boundary and is bound at each of its ends by a bounding pixel; defining a finite filter having a length correlated with the length of the ROI; defining a filtering pixels expansion that uniquely determines the padding values of the finite length filtered pixels ROI for the finite length filtering; and filtering the ROI pixels using the finite filter and the filtering expansion.

Abstract: A method for removing blocking artifacts from moving and still pictures, comprising classifying horizontal and vertical boundaries in each picture as blocky or non-blocky; for each blocky boundary, defining an adaptive, picture content-dependent, one-dimensional filtered pixels region of interest (ROI) that crosses the boundary and is bound at each of its ends by a bounding pixel; defining a finite filter having a length correlated with the length of the ROI; defining a filtering pixels expansion that uniquely determines the padding values of the finite length filtered pixels ROI for the finite length filtering; and filtering the ROI pixels using the finite filter and the filtering expansion.

Abstract: A method for removing blocking artifacts from moving and still pictures, comprising classifying horizontal and vertical boundaries in each picture as blocky or non-blocky; for each blocky boundary, defining an adaptive, picture content-dependent, one-dimensional filtered pixels region of interest (ROI) that crosses the boundary and is bound at each of its ends by a bounding pixel; defining a finite filter having a length correlated with the length of the ROI; defining a filtering pixels expansion that uniquely determines the padding values of the finite length filtered pixels ROI for the finite length filtering; and filtering the ROI pixels using the finite filter and the filtering expansion.