Microsoft Technology Licensing, LLC

Weidong Zhao

Aleksandar Uzelac

David Alan Stevens

Yongjun Wu

Takahiro Shigemitsu

John Graham Pierce

Briggs A Willoughby

Shyam Sadhwani

Craig Steven Ranta

Eric Christoffersen

Pravin Kumar Santiago

Timothy Allen Wright

Nikola Borisov

Jeffrey C Maier

Stanimir Naskov Kirilov

Yaming He

Juan Carlos Arevalo Baeza

William Chen

Andrew David Wilson

Chih-Lung Lin

Jesse Clay Satterfield

Naveen Thumpudi

Sanjeev Mehrotra

Sean M Callahan

Hrvoje Benko

Shijun Sun

Thomas W Holcomb

Robert J Jarrett

Chaitanya Dev Sareen

Jensen Michael Harris

David Andrew Matthews

Robert T Perry

Costin Hagiu

Jayashree Sadagopan

Yegnaswamy Sermadevi

Nigel Stuart Keam

Gary Caldwell

Jennifer Nan

Andrey Borissov Batchvarov

Cheng Chang

Ruchi Bhargava

Barry Friemel

John Lawrence Miller

Daniel Dinu

William R Sanders

David A Stevens

Nicola Borisov

Thomas P Russo

Examples are disclosed herein that are related to providing extended functionalities on-demand to an audio-based wearable device. One example provides a wearable computing device including an acoustic receiver configured to receive speech inputs, a speaker configured to present audio outputs, a communications subsystem configured to connect to an external device, a logic subsystem configured to execute instructions, and a storage subsystem having instructions executable by the logic subsystem to execute a program, connect to the external device via a wireless communications protocol, conduct an audio-based interaction of the program via the speech inputs received at the acoustic receiver and the audio outputs provided by the speaker, upon reaching a screen-based interaction of the program, notify a user via the speaker to interact with the external device, and provide image data to the external device for presentation via a screen of the external device.

External visual interactions for speech-based devices

Video image stabilization provides better performance on a generic platform for computing devices by evaluating available multimedia digital signal processing components, and selecting the available components to utilize according to a hierarchy structure for video stabilization performance for processing parts of the video stabilization. The video stabilization has improved motion vector estimation that employs refinement motion vector searching according to a pyramid block structure relationship starting from a downsampled resolution version of the video frames. The video stabilization also improves global motion transform estimation by performing a random sample consensus approach for processing the local motion vectors, and selection criteria for motion vector reliability. The video stabilization achieves the removal of hand shakiness smoothly by real-time one-pass or off-line two-pass temporal smoothing with error detection and correction.

Motion vector estimation for video image stabilization

Technologies are described for performing equation-based rate control using delay. For example, an observed network data rate and a network delay can be obtained for a network communication. A target network data rate can be calculated using the observed network data rate and the network delay. The target network data rate is calculated using an equation-based approach. For example, the equation-based rate control can set the target network data rate to a value that is directly proportional to the observed network data rate and inversely related to the network delay. The target network data rate is used to set the bitrate for the network communication.

Equation-based rate control using network delay for variable bitrate scenarios

Generic platform video image stabilization

Technologies are described for performing hybrid rate control that switches between a delay-based mode and a passive loss-based mode for a flow of network traffic. The switching can be performed based on the presence of loss-based TCP network flows. For example, rate control can be performed for a flow of network traffic in a delay-based mode. When the presence of a loss-based TCP network flow is detected, the flow of network traffic can be switched from the delay-based mode to a passive loss-based mode and rate control can be performed in the passive loss-based mode. When the loss-based TCP flow is no longer detected, the flow of network traffic can be switched back to the delay-based mode.

Delay based congestion control protocol co-existing with TCP

Touchscreen testing techniques are described. In one or more implementations, a piece of conductor (e.g., metal) is positioned as proximal to a touchscreen device and the touchscreen device is tested by simulating a touch of a user. This technique may be utilized to perform a variety of different testing of a touchscreen device, such as to test latency and probabilistic latency. Additional techniques are also described including contact geometry testing techniques.

Contact geometry tests

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

Touchscreen testing techniques are described. In one or more implementations, a conductor is placed proximal to a touchscreen device and the touchscreen device is tested by simulating a touch of a user by placing the conductor in a grounded state and lack of a touch by the user by placing the conductor in an ungrounded state.

Touchscreen testing

Video decoding implementations for a graphics processing unit

Neighbor determination in video decoding

In embodiments of prediction-based touch contact tracking, touch input sensor data is recognized as a series of components of a contact on a touch-screen display. A first component of the contact can be identified, and a second component can be determined to correlate to the contact. The first component and the second component can then be associated to represent a tracking of the contact. Subsequent components of the contact can be determined and associated with the previous components of the contact to further represent the tracking of the contact.

Prediction-based touch contact tracking

Probabilistic latency modeling

Decoding tasks are identified for decoding encoded video. Decoding tasks may include entropy decoding tasks, motion compensation tasks, inverse frequency transform tasks, inverse quantization tasks, intra decoding tasks, loop filtering tasks, or other tasks. Task dependencies are identified for the video decoding tasks. For example, one or more decoding tasks may depend on prior completion of entropy decoding tasks. The decoding tasks are prioritized based at least in part on the task dependencies. For example, a higher priority may be assigned to tasks that must be completed before other tasks that depend on them can begin. Prioritized decoding tasks are selected to be performed by hardware threads. For example, a first hardware thread may perform a first decoding task that does not depend on any uncompleted tasks while a second hardware thread performs a second decoding task that does not depend on any uncompleted tasks.

Scalable multi-thread video decoding

Growing community of inventors

Bellevue, WA, United States of America

Weidong Zhao