Santa Clara University

Kwai Inc.

Yifei Pei

Ying Liu

Lingzhi Liu

Nam Ling

Yongxiong Ren

Ming Kai Hsu

A class-specific neural network for video compressed sensing and methods for training and testing the class-specific neural network are provided. The class-specific neural network includes a Gaussian-mixture model (GMM) and a plurality of encoders, where the GMM classifies video frame blocks with a plurality of clusters and assigns the video frame blocks to the plurality of clusters. Further, the plurality of encoders receive the video frame blocks and generate a plurality of compressed-sensed frame block vectors, where the plurality of encoders correspond to the plurality of clusters.

Class-specific neural network for video compressed sensing

A neural network system, a method and an apparatus for image compression are provided. The neural network may include a generator including an encoder, an entropy estimator, and a decoder, where the encoder receives an input image and generates an encoder output, a plurality of quantized feature entries are obtained based on the encoder output outputted at a last encoder block, the entropy estimator receives the plurality of quantized feature entries and calculates an entropy loss based on the plurality of quantized feature entries, and the decoder receives the plurality of quantized feature entries and generates a reconstructed image. Furthermore, the neural network may include a discriminator that determines whether the reconstructed image different from the input image based on a discriminator loss. Moreover, the generator may determine whether content of the reconstructed image matches content of the input image based on a generator loss including the entropy loss.

End-to-end deep generative network for low bitrate image coding

Growing community of inventors

Santa Clara, CA, United States of America

Yifei Pei