Abstract: A computer implemented method for training a learning model by a distributed learning system includes computing nodes. The computing nodes respectively implement the learning model and deriving a gradient information for updating the learning model based on training data. The method involves: encoding, by the computing nodes, the gradient information by exploiting a correlation across the gradient information from the respective computing nodes; exchanging, by the computing nodes, the encoded gradient information within the distributed learning system; determining an aggregate gradient information based on the encoded gradient information from the computing nodes; and updating the learning model of the computing nodes with the aggregate gradient information, thereby training the learning model.

Abstract: In a method of generating a measure of video quality, a set of weightings (160) for a plurality of objective quality metrics is obtained. The objective quality metrics have themselves been calculated from a plurality of measurable objective properties (120) of video data files (100). The weightings (160) have been determined by fitting the objective quality metrics to a set comprising a ground-truth quality rating of each of the video data files coming from human scoring of quality (100). The method includes receiving a target video data file (180), the quality of which is to be measured. Values are calculated for the objective quality metrics (220) on the target video data file (180). The measure of video quality (240) is generated by combining the values for the objective quality metrics (220) on the target video data file (180) using the obtained set of weightings (160).

Abstract: A hash-based distributed video coding architecture has an encoder with an input video sequence organized in Groups of Pictures (GOPs) decomposed into key frames using H264/AVC Intra frame coding. The Wyner-Ziv (WZ) frames are encoded in two parts, a hash layer and a WZ layer. The WZ frames are quantized and each and then decorrelated using spatio-temporal prediction and entropy coded. The intra bit stream is H264/AVC decoded and the intra frames are stored in a reference frame buffer. The hash is decoded by inverting the tasks applied at the encoder, and the obtained bit planes are stored. Overlapped Block Motion Estimation and Probalistic Compensation (OBMEPC) is used to estimate the missing bit planes in the side-information. The decoder utilizes the hash information and the side information frame created by OBMEPC to perform online estimation of the correlation channel, and produces soft estimates used to decode the WZ bit planes.