Microsoft Technology Licensing, LLC

Yegnaswamy Sermadevi

Weidong Zhao

Yongjun Wu

Eric Christoffersen

Juan Carlos Arevalo Baeza

Cheng Chang

Sean M Callahan

Video decoding innovations for multithreading implementations and graphics processor unit ('GPU') implementations are described. For example, for multithreaded decoding, a decoder uses innovations in the areas of layered data structures, picture extent discovery, a picture command queue, and/or task scheduling for multithreading. Or, for a GPU implementation, a decoder uses innovations in the areas of inverse transforms, inverse quantization, fractional interpolation, intra prediction using waves, loop filtering using waves, memory usage and/or performance-adaptive loop filtering. Innovations are also described in the areas of error handling and recovery, determination of neighbor availability for operations such as context modeling and intra prediction, CABAC decoding, computation of collocated information for direct mode macroblocks in B slices, reduction of memory consumption, implementation of trick play modes, and picture dropping for quality adjustment.

Reducing memory consumption during video decoding

Neighbor determination in video decoding

Parallel programming and execution systems and techniques are discussed herein. In accordance with one technique, a parallel programming model is used to decompose a complex process into parallelizable tasks with proper dependencies set by data objects as interfaces. Another technique features a scheduling mechanism that provides thread affinity and automatic load-balance management among an assortment of processing units such as CPUSs/CPU cores, GPUs/GPU cores, and/or hardware threads in a scalable manner. The models and techniques are applied in an exemplary manner to a digital media encoding process.

Parallel programming and execution systems and techniques

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Mountain View, CA, United States of America

Yegnaswamy Sermadevi