A new compressive sensing video coding framework based on Gaussian mixture model

作者：

Highlights：

• A new efficient coding framework for compressive sensing video (CSV) is proposed.

• The proposed framework is based on GMM and product vector quantizer.

• The proposed framework achieves a significant coding rate-distortion improvement.

• The computational complexity of the proposed CSV encoding algorithm is low.

摘要

•A new efficient coding framework for compressive sensing video (CSV) is proposed.•The proposed framework is based on GMM and product vector quantizer.•The proposed framework achieves a significant coding rate-distortion improvement.•The computational complexity of the proposed CSV encoding algorithm is low.

论文关键词：Compressive sensing video,Gaussian mixture model,Lossy compression,Video coding,Product vector quantizer

论文评审过程：Received 29 April 2016, Revised 16 February 2017, Accepted 13 March 2017, Available online 18 March 2017, Version of Record 5 April 2017.

论文官网地址：https://doi.org/10.1016/j.image.2017.03.009