Microsoft Technology Licensing, LLC

Sergey Smirnov

Ming-Chieh Lee

James David Johnston

Naveen Thumpudi

Wei-ge Chen

Brian D Crites

Sudheer Sirivara

Chris Messer

Shou-Jen (Joseph) Wu

Described techniques and tools include techniques and tools for mapping digital media data (e.g., audio, video, still images, and/or text, among others) in a given format to a transport or file container format useful for encoding the data on optical disks such as digital video disks (DVDs). A digital media universal elementary stream can be used to map digital media streams (e.g., an audio stream, video stream or an image) into any arbitrary transport or file container, including optical disk formats, and other transports, such as broadcast streams, wireless transmissions, etc. The information to decode any given frame of the digital media in the stream can be carried in each coded frame. A digital media universal elementary stream includes stream components called chunks. An implementation of a digital media universal elementary stream arranges data for a media stream in frames, the frames having one or more chunks.

Digital media universal elementary stream

An encoder compresses a set of still images and parameterizes (with effect parameters) additional images synthesized from the set of still images. A decoder decompresses the set of still images and synthesizes the additional images using the effect parameters.

Image compression and synthesis for video effects

Adaptive entropy encoding and decoding techniques are described. For example, a screen capture encoder and decoder perform adaptive entropy encoding and decoding of palettized screen capture content in screen capture video. The encoder selects between different entropy encoding modes (such as an arithmetic coding mode and a combined run length/Huffman encoding mode, which allow the encoder to emphasize bitrate reduction at some times and encoding speed at other times). The run length encoding can use run value symbols adapted to common patterns of redundancy in the content. When the encoder detects series of pixels that could be encoded with different, alternative runs, the encoder selects between the alternative runs based upon efficiency criteria. The encoder also performs adaptive Huffman encoding, efficiently parameterizing Huffman code tables to reduce overall bitrate while largely preserving the compression gains of the adaptive Huffman encoding. A decoder performs converse operations.

Adaptive entropy encoding/decoding for screen capture content

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Redmond, WA, United States of America

Sergey Smirnov