Ati Technologies Ulc

Advanced Micro Devices Corporation

Boris Ivanovic

Mehdi Saeedi

Gabor Sines

Ihab Amer

Yang Liu

Allen J Porter

Haibo Liu

Arshad Rahman

Jinbo Qiu

Rajeevan Panchacharamoorthy

David I J Glen

Syed Athar Hussain

Edward A Harold

Yubao Zheng

Jun Lei

Ho Hin Lau

Kyle Plumadore

David Galiffi

Sahar Alipour Kashi

Fatemeh Amirnavaei

Edward George Callway

Skyler Jonathon Saleh

Lei Zhang

Ruijin Wu

Guennadi Riguer

Ihab M A Amer

Wei Gao

Jiao Wang

Crystal Yeong-Pian Sau

Fabio Gulino

Zhiqi Hao

Arash Hariri

Catalin Beju

Xingping Cao

Tomasz Stolarczyk

Tiberiu Visan

A method includes obtaining, at a data reduction module, metrics of a first block of an input video frame and a second block of a reference frame. The data reduction module includes an analysis module and a filter. A perceptual importance of the first block of the input video frame is determined at the analysis module using the metrics. An entropy of the input video frame provided to an encoder is adjusted at the filter of the data reduction module based on the perceptual importance of the first block of the input video frame.

Metric driven adaptive processing for video encoders

Systems, apparatuses, and methods for implementing spatial block-level pixel activity extraction optimization leveraging motion vectors are disclosed. Control logic coupled to an encoder generates block-level pixel activity metrics for a new frame based on the previously calculated block-level pixel activity data from a reference frame. A cost is calculated for each block of a new frame with respect to a corresponding block of the reference frame. If the cost is less than a first threshold, then the control logic generates an estimate of a pixel activity metric for the block which is equal to a previously calculated pixel activity metric for a corresponding block of the reference frame. If the cost is greater than the first threshold but less than a second threshold, an estimate of the pixel activity metric is generated by extrapolating from the previously calculated pixel activity metric.

Spatial block-level pixel activity extraction optimization leveraging motion vectors

A processing system estimates motion between blocks of images based on differences in pixel activities between blocks. Blocks having similar pixel activities are more likely to be matches for motion estimation than are blocks having dissimilar pixel activities. Accordingly, the processing system compares pixel activity between current block of a current image and candidate blocks within a search area in the reference image, and estimates motion based on a subset of candidate blocks having a difference in pixel activity from that of the current block within a threshold.

Motion estimation using pixel activity metrics

A multimedia system allocates, during encoding of a multimedia stream, bits to portions of frames based on quality metrics and bit usages for different quantization parameters (QPs). An encoder of the multimedia system encodes a frame in a first pass with a first QP and in a second pass with a second QP. A comparator of the multimedia system measures and compares quality metrics, such as mean squared error, for each portion of the frame encoded with the first QP and the second QP. The comparator compares the difference between the quality metrics for each portion encoded with each QP to a threshold. If the difference in quality metrics for a portion exceeds the threshold, the comparator selects the portion for inclusion in a subset of portions to be encoded with the second QP.

Benefit-based bitrate distribution for video encoding

A display system modifies display cycles of one or more displays to perform a system operation while avoiding visual perturbations at the one or more displays. The display system modifies, synchronizes, or both, blanking periods of the one or more displays such that blanking periods equal or exceed a blackout duration and overlap for at least the blackout duration. Then the system performs the system operation during an overlapping portion of the one or more blanking periods, where the system operation reduces availability of display data at the one or more displays.

Display cycle control system

An encoder calculates a first local encoding parameter for a first block of video content based on one or more local metrics. The encoder modifies the first local encoding parameter based on one or more second local encoding parameters for one or more second blocks of video content that are adjacent to the first block of video content. The encoder then encodes the first block using the modified first local encoding parameter. In some cases, the local encoding parameters are quantization parameters used to quantize values of pixels or compression parameters used to compress values of the pixels. The local metric can include one or more of a target bit rate, a texture complexity, a contrast, an indicator of motion in the first block, and an importance map.

Consistent adjustment of parameters for video encoding

Systems, apparatuses, and methods for bit budgeting in video encode pre-analysis based on context and features are disclosed. A pre-encoder receives a video frame and evaluates each block of the frame for the presence of several contextual indicators. The contextual indicators can include memory colors, text, depth of field, and other specific objects. For each contextual indicator detected, a coefficient is generated and added with other coefficients to generate a final importance value for the block. The coefficients can be adjusted so that only a defined fraction of the picture is deemed important. The final importance value of the block is used to determine the bit budget for the block. The block bit budgets are provided to the encoder and used to influence the quantization parameters used for encoding the blocks.

Video encode pre-analysis bit budgeting based on context and features

A feedback processing module includes a memory configured to store feedback received from an encoder. The feedback includes parameters associated with encoded graphics content generated by a graphics engine. The feedback processing module also includes a processor configured to generate configuration information for the graphics engine based on the feedback. The graphics engine is configured to execute a workload based on the configuration information. In some cases, the feedback processing module is also configured to receive feedback from a decoder that is used to decode the graphics content that is encoded by the encoder and generate the configuration information based on the feedback received from the decoder.

Graphics rendering with encoder feedback

Systems, apparatuses, and methods for performing asynchronous memory clock changes on multiple displays are disclosed. From time to time, a memory clock frequency change is desired for a memory subsystem storing frame buffer(s) used to drive pixels to multiple displays. For example, when the real-time memory bandwidth demand differs from the memory bandwidth available with the existing memory clock frequency, a control unit tracks the vertical blanking interval (VBI) timing of a first display. Also, the control unit causes a second display to enter into panel self-refresh (PSR) mode. Once the PSR mode of the second display overlaps with a VBI of the first display, a memory clock frequency change, including memory training, is initiated. After the memory clock frequency change, the displays are driven by the frame buffer(s) in the memory subsystem at an updated frequency.

Performing asynchronous memory clock changes on multi-display systems

A processing device for executing a machine learning neural network operation includes memory and a processor. The processor is configured to receive input data at a layer of the machine learning neural network operation, receive a plurality of sorted filters to be applied to the input data, apply the plurality of sorted filters to the input data to produce a plurality of different feature maps, compress the plurality of different feature maps according to a similarity of the feature maps relative to each other and store the plurality of different feature maps in the memory.

Feature reordering based on similarity for improved memory compression transfers during machine learning jobs

The present disclosure is directed to techniques for determining a perceptual importance map. The perceptual importance map indicates the relative importance to the human visual system of different portions of an image. The techniques include obtaining cost values for the blocks of an image, where cost values are values used in determining motion vectors. For each block, a confidence value is derived from the cost values. The confidence value indicates the confidence with which the motion vector is believed to be correct. A perceptual importance value is determined based on the confidence value via one or more modifications to the confidence value to better reflect importance to the human visual system. The generated perceptual importance values can be used for various purposes such as allocating bits for encoding, identifying regions of interest, or selectively rendering portions of an image with greater or lesser detail based on relative perceptual importance.

Perceptual importance maps for image processing

Systems, apparatuses, and methods for generating a model for determining a quantization strength to use when encoding video frames are disclosed. A pre-encoder performs multiple encoding passes using different quantization strengths on a portion or the entirety of one or more pre-processed video frames. The pre-encoder captures the bit-size of the encoded output for each of the multiple encoding passes. Then, based on the multiple encoding passes, the pre-encoder generates a model for mapping bit-size to quantization strength for encoding video frames or portion(s) thereof. When the encoder begins the final encoding pass for one or more given video frames or any portion(s) thereof, the encoder uses the model to map a preferred bit-size to a given quantization strength. The encoder uses the given quantization strength when encoding the given video frame(s) or frame portion(s) to meet a specified bit-rate for the encoded bitstream.

Content adaptive quantization strength and bitrate modeling

A technique for determining a quality value for a subject block of encoded video is provided. Contributing blocks, of the same frame and/or different frames of the subject block, are determined by identifying blocks likely to be a part of the same moving object or background as the subject block. A spatial and/or temporal filter is then applied to the quality values of the contributing blocks and an initial quality value of the subject block. With a spatial filter, quality values for contributing blocks from the same frame are combined and used to modify the quality value of the subject block. With a spatial filter, a temporal characteristic quality value for contributing blocks of one or more other frames (such as the immediately previous frame) is determined and then combined with a quality value representative of the subject block.

Determining quality values for blocks of encoded video

A system and method for dynamically changing encode quality at a block level based on runtime pre-encoding analysis of content in a video stream. A video encoder continuously analyzes the content during runtime, and collects statistics and/or characteristics of the content before encoding it. This classifies the block among pre-defined categories of content, where every category has its own compression parameters.

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Markham, Canada

Boris Ivanovic