Google Inc.

Mitsubishi Electric Research Laboratories, Inc.

Yongzhe Wang

Debargha Mukherjee

Anthony Vetro

Jingning Han

Dong Tian

Hassan Mansour

Antonio Ortega

Anthony Mai

A content system identifies shots in a first video and shots in a second video. Shot durations are determined for the identified shots of each video. A histogram is generated for each video, each histogram dividing the identified shots of the corresponding video into a set of buckets divided according to a range of shot durations. The system determines confidence weights for the buckets of each histogram, with the confidence weight for a bucket based on a likelihood of a particular number of identified shots occurring within the range of shot duration for that bucket. A correlation value is computed for the two videos based on a number of identified shots in each bucket of each respective histogram and based on the confidence weights. The content system determines whether the two videos are similar based on the correlation value and a self-correlation value of each video.

Determination of similarity between videos using shot duration correlation

Encoding and decoding using prediction dependent transform coding are provided. Encoding and decoding using prediction dependent transform coding may include identifying a current input block from a current input frame from an input video stream, generating a prediction block for the current input block, generating a residual block based on a difference between the current input block and the prediction block, generating, by a processor in response to instructions stored on a non-transitory computer readable medium, an encoded block by encoding the residual block based on the prediction block using the using prediction dependent transform coding, including the encoded block in an output bitstream, and outputting or storing the output bitstream.

Prediction dependent transform coding

A method processes a signal represented as a graph by first determining a graph spectral transform based on the graph. In a spectral domain, parameters of a graph filter are estimated using a training data set of unenhanced and corresponding enhanced signals. The graph filter is derived based on the graph spectral transform and the estimated graph filter parameters. Then, the signal is processed using the graph filter to produce an output signal. The processing can enhance signals such as images by denoising or interpolating missing samples.

Method for estimating parameters of a graph spectral filter using training data

An image for a virtual view of a scene is generated based on a set of texture images and a corresponding set of depth images acquired of the scene. A set of candidate depths associated with each pixel of a selected image is determined. For each candidate depth, a cost that estimates a synthesis quality of the virtual image is determined. The candidate depth with a least cost is selected to produce an optimal depth for the pixel. Then, the virtual image is synthesized based on the optimal depth of each pixel and the texture images. The method also applies first and second depth enhancement before, and during view synthesis to correct errors or suppress noise due to the estimation or acquisition of the dense depth images and sparse depth features.

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Los Angeles, CA, United States of America

Yongzhe Wang