Qualcomm Incorporated

Meta Platforms, Inc.

Hariharan Ganesh Lalgudi

In Suk Chong

Harikrishna Madadi Reddy

Sumit Mohan

Gaurang Chaudhari

Marta Karczewicz

Xianglin Wang

Athanasios Leontaris

Narendranath Malayath

Kai Wang

Yunqing Chen

Feng Ge

Jaehong Chon

Ling Feng Huang

Mang Li

Hari Ganesh Thirunageswaram

A mode decision component is configured to determine the costs of different modes for a selected partition of a frame of a video using an estimated compression coding data, which is calculated prior to a corresponding actual compression coding data being calculated based on another partition immediately prior to the selected partition in a partition processing order. The estimated compression coding data is determined based on a previously calculated compression coding data calculated based on a completed partition prior to the selected partition in the partition processing order. The mode decision component is configured to use the determined costs to select one of the modes. An encoder component is configured to use the selected mode to encode the selected partition by using the corresponding actual compression coding data calculated based on the another partition immediately prior to the selected partition in the partition processing order.

Architecture to adapt cumulative distribution functions for mode decision in video encoding

A method for sharing the motion estimation and mode decision results and decisions of one codec with another codec is disclosed. A video is received to be transcoded into a plurality of different output encodings of a plurality of different codecs. Each codec has a different video encoding format. A shared motion estimation and a shared mode decision processing of the video are performed. One or more results of the shared mode decision processing shared across the plurality of different codecs are used to encode the video into the plurality of different output encodings of the plurality of different codecs.

Cross-codec encoding optimizations for video transcoding

At least one processor is configured to encode samples of a largest coding unit (LCU) of a picture using a sample adaptive offset (SAO) mode. To encode the samples of the LCU using SAO, the at least one processor is configured to: calculate differences between corresponding reconstructed samples of the LCU and original samples of the LCU, clip a number of bits from each of the differences to form clipped differences, sum the clipped differences to form a sum of differences, clip the sum of differences to form a clipped sum of differences, calculate a number of the reconstructed samples, clip a number of bits from the number of reconstructed samples to form a dipped number of samples, and divide the clipped sum of differences by the clipped number of samples to produce an offset for the LCU.

Low complexity sample adaptive offset encoding

Implementations include video image processing systems, methods, and apparatus for integrated video downscale in a video core. The downscaler computes and writes a display frame to an external memory. This frame may have the same resolution as a target display device (e.g., mobile device). The target display device then reads this display frame, rather than the original higher resolution frame. By enabling downscale during encoding/decoding, the device can conserve resources such as memory bandwidth, memory access, bus bandwidth, and power consumption associated with separately downscaling a frame of video data.

Integrated downscale in video core

A video coding device configured according to some aspects of this disclosure includes a memory configured to store a plurality of motion vector candidates. Each motion vector candidate can corresponding to at least one of a plurality of prediction units (PUs) partitioned in a parallel motion estimation region (MER). The video coding device also includes a processor in communication with the memory. The processor is configured to select a subset of the plurality of motion vector candidates to include in a merge candidate list. The selection can be based on a priority level of each motion vector candidate. The processor can be further configured to generate the merge candidate list to include the selected motion vector candidates.

Scalable implementation for parallel motion estimation regions

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San Diego, CA, United States of America

Hariharan Ganesh Lalgudi