Abstract: A message suggestion service may use clusters of pre-approved messages to improve the quality of messages suggested to users. During a conversation, messages of the conversation may be processed with a neural network to compute a conversation encoding vector. The neural network may also be used to compute pre-approved message encoding vectors of the pre-approved messages. Distances between the conversation encoding vector and the pre-approved message encoding vectors may be used to select one or more clusters. Distances between the conversation encoding vector and the pre-approved message encoding vectors may then be used to select one or more pre-approved messages from the selected clusters. The selected pre-approved messages may then be presented as suggested messages to a user.

Abstract: A neural network learning algorithm obtains a fingerprint of a video sequence. Using the fingerprint, selection of an appropriate video stabilization algorithm, and its parameter settings, is performed. The video thus stabilized is made more appealing for viewing and easier for subsequent video compression. The neural network may be trained during a training phase to select a correct fingerprint of each video sequence based on the effectiveness of resulting video stabilization and/or compression.

Abstract: Video foreground and background detection is performed on a received video sequence on a macroblock by macroblock basis using motion vector encoding information associated with each macroblock. Some of the macroblocks in each frame, for example, edge or corner macroblocks, may be exempt from the foreground and background detection. The detection information, along with the video data, is passed to an image stabilization algorithm that prepares the video for subsequent video compression.

Abstract: A neural network learning algorithm obtains a fingerprint of a video sequence. Using the fingerprint, selection of an appropriate video stabilization algorithm, and its parameter settings, is performed. The video thus stabilized is made more appealing for viewing and easier for subsequent video compression. The neural network may be trained during a training phase to select a correct fingerprint of each video sequence based on the effectiveness of resulting video stabilization and/or compression.

Abstract: Video foreground and background detection is performed on a received video sequence on a macroblock by macroblock basis using motion vector encoding information associated with each macroblock. Some of the macroblocks in each frame, for example, edge or corner macroblocks, may be exempt from the foreground and background detection. The detection information, along with the video data, is passed to an image stabilization algorithm that prepares the video for subsequent video compression.