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Circuits, Systems, and Signal Processing

, Volume 37, Issue 8, pp 3537–3557 | Cite as

Block Matching Video Compression Based on Sparse Representation and Dictionary Learning

  • Maziar Irannejad
  • Homayoun Mahdavi-Nasab
Article
  • 101 Downloads

Abstract

This work presents a video compression method based on sparse representation and dictionary learning algorithms. The proposed scheme achieves superb rate-distortion performance and decent subjective quality, compared to modern standards, especially at low bit-rates. Different from similar works, sparse representation is employed here for both intra-frame and block matching inter-frame motion information. Dividing video frames to reference and current frames, motion vectors and motion compensation residuals of current frames are estimated in regard to reference frames. The sparse codes of reference frames and motion compensation residuals are obtained using learned dictionaries, entropy-coded, and stored or sent to the receiver along with the coded motion field. In the receiver, after decoding the sparse codes and motion vectors, the reference frames and residuals are reconstructed employing the same learned dictionary and the current frames are recovered using the reference frames and motion fields. In the proposed scheme, the Iterative Least Square Dictionary Learning Algorithm (ILS-DLA) and K-SVD dictionary building methods are employed in the DCT domain. The compression rate and quality of the method based on the two dictionary learning algorithms are compared to each other and to H.264/AVC and HEVC modern standards. The results based on PSNR and SSIM criteria show that the proposed approach presents superior performance respect to H.264/AVC and even HEVC for higher bit-rates of QCIF video format, and the K-SVD learning algorithm performs slightly better than the ILS-DLA for the purpose.

Keywords

Sparse representation Dictionary learning K-SVD ILS-DLA Block matching 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Digital Processing and Machine Vision Research Center, Najafabad BranchIslamic Azad UniversityNajafabadIran
  2. 2.Department of Electrical Engineering, Najafabad BranchIslamic Azad UniversityNajafabadIran

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