Abstract: The present disclosure generally relates to image processing. For example, aspects of the present disclosure include systems and techniques for performing spatial and temporal processing of image data. Certain aspects provide an apparatus for processing frame data. The apparatus generally includes a memory, and one or more processors coupled to the memory, the one or more processors configured to: perform a first noise reduction operation based on first frame data via a machine learning component to generate first processed frame data; generate first feedback data based on the first processed frame data; and perform, via the machine learning component, a second noise reduction operation based on second frame data and the first feedback data.

Abstract: Temporal filtering operations may be reset for certain pixels within an image frame to reduce contribution from previous input frames to reduce ghosting and other artifacts. The resetting reduces the contribution to, for example, zero, either immediately or within a predetermined period of time (e.g., a certain number of frames). A decision regarding whether to reset temporal filtering for a pixel of the image frame may be based on a probability assigned to that pixel. The probability can be based on rules with one or more criteria. One example factor for adjusting probability is a confidence level regarding the temporal filtering decision for the pixel, in which the probability for a random reset of a pixel is based on the confidence level regarding the temporal filtering decision for those pixels.

Abstract: Temporal filtering operations may be reset for certain pixels within an image frame to reduce contribution from previous input frames to reduce ghosting and other artifacts. The resetting reduces the contribution to, for example, zero, either immediately or within a predetermined period of time (e.g., a certain number of frames). A decision regarding whether to reset temporal filtering for a pixel of the image frame may be based on a probability assigned to that pixel. The probability can be based on rules with one or more criteria. One example factor for adjusting probability is a confidence level regarding the temporal filtering decision for the pixel, in which the probability for a random reset of a pixel is based on the confidence level regarding the temporal filtering decision for those pixels.

Abstract: In general, techniques are described regarding selective allocation of processing resources for processing image data. Processing circuitry may warp a frame of image data around variously indicated areas of the frame to create a warped frame of image data. The processing circuitry may allocate more pixels to the indicated areas by virtue of the frame warping operation. The warped frame may then be output for further processing in accordance with proportions of the warped frame resulting in better image or video quality in the indicated areas.

Abstract: In general, techniques are described regarding selective allocation of processing resources for processing image data. Processing circuitry may warp a frame of image data around variously indicated areas of the frame to create a warped frame of image data. The processing circuitry may allocate more pixels to the indicated areas by virtue of the frame warping operation. The warped frame may then be output for further processing in accordance with proportions of the warped frame resulting in better image or video quality in the indicated areas.

Abstract: A device and methods are provided for accessing memory. In one embodiment, a method includes receiving a request for data stored in a device, checking a local memory for data based on the request to determine if one or more blocks of data associated with the request are stored in the local memory, and generating a memory access request for one or more blocks of data stored in a memory of the device based when one or more blocks of data are not stored in the local memory. In one embodiment, data stored in memory of the device may be arranged in a configuration to include a plurality of memory access units each having adjacent lines of pixel data to define a single line of memory within the memory access units. Memory access units may be configured based on memory type and may reduce the number of undesired pixels read.