Dictionary learning based reconstruction for distributed compressed video sensing

• Leveraging more realistic video signal models that go beyond simple sparsity.
• A novel undersampling correlation noise model for subsampled video signals.
• To learn a dictionary that efficiently describes the video contents and structures.
• A maximum-likelihood (ML) dictionary learning based reconstruction for DCVS.
• Signal recovery is performed within ML learning, not as an independent task.

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient reconstruction by leveraging more realistic signal models that go beyond simple sparsity is still an open challenge. In this paper, we propose a novel “undersampled” correlation noise model to describe compressively sampled video signals, and present a maximum-likelihood dictionary learning based reconstruction algorithm for DCVS, in which both the correlation and sparsity constraints are included in a new probabilistic model. Moreover, the signal recovery in our algorithm is performed during the process of dictionary learning, instead of being employed as an independent task. Experimental results show that our proposal compares favorably with other existing methods, with 0.1–3.5 dB improvements in the average PSNR, and a 2–9 dB gain for non-key frames when key frames are subsampled at an increased rate.