Joint Decoding of Independently Encoded Compressive Multi-View Video Streams


We design a video coding and decoding framework for multi-view video systems based on compressed sensing imaging principles. Specifically, we focus on joint decoding of independently encoded compressively-sampled multi-view video streams. We first propose a novel distributed coding/decoding architecture designed to leverage inter-view correlation through joint decoding of the received compressively-sampled frames. At the encoder side, we select one view (referred to as K-view) as a reference for the other views (referred to as CS-views). The video frames of the CS-view are encoded and transmitted at a lower measurement rate than those of the selected K-view. At the decoder side, we generate side information to decode the CS-views as follows. First, each K-view frame is down-sampled and reconstructed, and then compared with the initially reconstructed CS-view frame to obtain an estimate of the inter-view motion vector. The original CS-view measurements are then fused with the generated side image to reconstruct the CS-view frame through a newly designed algorithm that operates in the measurement domain. We also propose a blind video quality estimation method that can be used within the proposed framework to design channel-adaptive rate control algorithms for quality-assured multi-view video streaming. We extensively evaluate the proposed scheme using real multi-view video traces. Results indicate that up to 1.6 dB improvement in terms of PSNR can be achieved by the proposed scheme compared with traditional independent decoding of CS frames.

Meeting Name

2013 Picture Coding Symposium, PCS 2013 (2013: Dec. 8-11, San Jose, CA)


Computer Science

Keywords and Phrases

Algorithms; Image Coding; Video Streaming, Channel Adaptive; Coding and Decoding; Distributed Coding; Imaging Principle; Multiview Video; Rate Control Algorithms; Side Information; Video Quality Estimations, Decoding

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Document Type

Article - Conference proceedings

Document Version


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© 2013 IEEE Computer Society, All rights reserved.

Publication Date

01 Dec 2013