Abstract: Innovations in video playback using a browser-based video decoder are described. In a computer system that includes multiple central processing units (“CPUs”), a browser-based video decoder performs operations with multiple threads that may execute simultaneously on different CPUs. The video decoder can perform decoding operations in parallel for different sections of a picture. For example, with a main CPU thread associated with a browser, the video decoder performs a first decoding workload (e.g., bitstream parsing) for a picture. With auxiliary CPU threads associated with Web workers and simultaneously executing on different CPUs, the video decoder performs a second decoding workload (e.g., entropy decoding, decoding of side information) for different sections of the picture, one section per auxiliary CPU thread. If the computer system also includes a graphics processing unit (“GPU”), the video decoder can perform additional decoding workloads with shader routines executable on the GPU.

Abstract: Innovations in motion estimation adapted for screen remoting scenarios are described. For example, a video encoder calculates a hash value for a current block in a current picture. The video encoder searches, subject to a spatial constraint, for a matching block in a reference picture (e.g., the previous picture in display order) based at least in part on the hash value for the current block. The spatial constraint defines a search area in the reference picture within which hash values for candidate blocks in the reference picture may be compared to the hash value for the current block. By using a spatial constraint to limit the range of the local hash-based motion estimation, the video encoder can speed up the motion estimation process while still considering the candidate blocks in the reference picture that are most likely to match the current block.

Abstract: Innovations in video decoding and rendering operations in a graphics pipeline, in which at least some of the operations are performed using a graphics processing unit (“GPU”), are described. For example, a video playback tool aggregates texture values for intra-coded blocks of a picture in central processing unit (“CPU”) memory, then transfers the texture values for the intra-coded blocks from the CPU memory to GPU memory. The video playback tool performs operations to decode the encoded data and reconstruct the picture. For a given block (e.g., of a macroblock, coding unit) of the picture, a graphics primitive represents texture values for the given block as a point for processing by the GPU. The video playback tool uses one or more shader routines, executable by the GPU, to transfer texture values to a display buffer. In some cases, the video playback tool also performs decoding operations with the shader routines.

Abstract: Innovations in motion estimation adapted for screen remoting scenarios are described herein. For example, as part of motion estimation for a current picture, a video encoder finds a pivot point in the current picture, calculates a hash value for the pivot point, and searches for a matching area in a previous picture. In doing so, the video encoder can calculate a hash index from the hash value and look up the hash index in a data structure to find candidate pivot points in the previous picture. The video encoder can compare the hash value for the pivot point in the current picture to a hash value for a candidate pivot point in the previous picture and, when the hash values match, compare sample values around the respective pivot points. In this way, the video encoder can quickly detect large areas of exact-match blocks having uniform motion.

Abstract: Innovations in video decoding and rendering operations for inter-coded blocks in a graphics pipeline, in which at least some of the operations are performed using a graphics processing unit (“GPU”), are described. For example, a video playback tool receives encoded data for a current picture and performs operations to decode the encoded data and reconstruct the current picture. For a given inter-coded block of the current picture, a graphics primitive represents texture values as a point for processing by the GPU. The graphics primitive can have one or more attributes, including a motion vector, a block size, a display index value (indicating a location in a display buffer), and/or a residual index value (indicating a location of residual values). The operations performed by the video playback tool can include interpolation of sample values at fractional-sample offsets and motion compensation performed for inter-coded blocks in multiple passes for different block sizes.

Abstract: Innovations in motion estimation adapted for screen remoting scenarios are described. For example, a video encoder calculates a hash value for a current block in a current picture. The video encoder searches, subject to a spatial constraint, for a matching block in a reference picture (e.g., the previous picture in display order) based at least in part on the hash value for the current block. The spatial constraint defines a search area in the reference picture within which hash values for candidate blocks in the reference picture may be compared to the hash value for the current block. By using a spatial constraint to limit the range of the local hash-based motion estimation, the video encoder can speed up the motion estimation process while still considering the candidate blocks in the reference picture that are most likely to match the current block.

Abstract: Innovations in video playback using a browser-based video decoder are described. In a computer system that includes multiple central processing units (“CPUs”), a browser-based video decoder performs operations with multiple threads that may execute simultaneously on different CPUs. The video decoder can perform decoding operations in parallel for different sections of a picture. For example, with a main CPU thread associated with a browser, the video decoder performs a first decoding workload (e.g., bitstream parsing) for a picture. With auxiliary CPU threads associated with Web workers and simultaneously executing on different CPUs, the video decoder performs a second decoding workload (e.g., entropy decoding, decoding of side information) for different sections of the picture, one section per auxiliary CPU thread. If the computer system also includes a graphics processing unit (“GPU”), the video decoder can perform additional decoding workloads with shader routines executable on the GPU.

Abstract: Innovations in motion estimation adapted for screen remoting scenarios are described herein. For example, as part of motion estimation for a current picture, a video encoder finds a pivot point in the current picture, calculates a hash value for the pivot point, and searches for a matching area in a previous picture. In doing so, the video encoder can calculate a hash index from the hash value and look up the hash index in a data structure to find candidate pivot points in the previous picture. The video encoder can compare the hash value for the pivot point in the current picture to a hash value for a candidate pivot point in the previous picture and, when the hash values match, compare sample values around the respective pivot points. In this way, the video encoder can quickly detect large areas of exact-match blocks having uniform motion.

Abstract: Innovations in video decoding and rendering operations for inter-coded blocks in a graphics pipeline, in which at least some of the operations are performed using a graphics processing unit (“GPU”), are described. For example, a video playback tool receives encoded data for a current picture and performs operations to decode the encoded data and reconstruct the current picture. For a given inter-coded block of the current picture, a graphics primitive represents texture values as a point for processing by the GPU. The graphics primitive can have one or more attributes, including a motion vector, a block size, a display index value (indicating a location in a display buffer), and/or a residual index value (indicating a location of residual values). The operations performed by the video playback tool can include interpolation of sample values at fractional-sample offsets and motion compensation performed for inter-coded blocks in multiple passes for different block sizes.

Abstract: Innovations in video decoding and rendering operations in a graphics pipeline, in which at least some of the operations are performed using a graphics processing unit (“GPU”), are described. For example, a video playback tool aggregates texture values for intra-coded blocks of a picture in central processing unit (“CPU”) memory, then transfers the texture values for the intra-coded blocks from the CPU memory to GPU memory. The video playback tool performs operations to decode the encoded data and reconstruct the picture. For a given block (e.g., of a macroblock, coding unit) of the picture, a graphics primitive represents texture values for the given block as a point for processing by the GPU. The video playback tool uses one or more shader routines, executable by the GPU, to transfer texture values to a display buffer. In some cases, the video playback tool also performs decoding operations with the shader routines.

Abstract: Embodiments are directed to providing a structural diagram to collect user input data in a non-linear manner and to managing multiple distributed application models using a structural diagram. In one scenario, a computer system receives a user input specifying a distributed software application that is to be managed across various different computer systems. The computer system determines, based the specified distributed application, which nodes are to be displayed in a structural diagram, where the nodes of the structural diagram represent application properties for managing the specified distributed software application. The computer system then provides a structural diagram that displays the determined nodes. The nodes allow non-linear configuration of the specified application across the various computer systems.

Abstract: Embodiments are directed to providing a structural diagram to collect user input data in a non-linear manner and to managing multiple distributed application models using a structural diagram. In one scenario, a computer system receives a user input specifying a distributed software application that is to be managed across various different computer systems. The computer system determines, based the specified distributed application, which nodes are to be displayed in a structural diagram, where the nodes of the structural diagram represent application properties for managing the specified distributed software application. The computer system then provides a structural diagram that displays the determined nodes. The nodes allow non-linear configuration of the specified application across the various computer systems.