Title: Dictionary-based intra-prediction framework for image compression via sparse representation

Authors: Arabinda Sahoo; Pranati Das

Addresses: Department of ECE, ITER, Siksha 'O' Anusandhan University, Bhubaneswar, Odisha, 751030, India; Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, 759145, India ' Department of ECE, ITER, Siksha 'O' Anusandhan University, Bhubaneswar, Odisha, 751030, India; Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, 759145, India

Abstract: Nowadays, image compression is very important for efficient data storage and transmission. This paper presents a dictionary-based intra-prediction framework for image compression using sparse representation, with the construction of trained over-complete dictionaries. The intra-prediction residuals selected from different images and K-SVD algorithm are used to train over-complete dictionaries. The trained dictionaries are integrated into the intra-prediction framework for efficient image compression. In this proposed method, first, intra-prediction is applied over an image and then prediction residuals of the image are encoded using sparse representation. Sparse approximation algorithm and trained dictionaries are employed for encoding of prediction residuals of the image. The coefficients obtained from sparse representation are used for encoding. For efficient sparse representation with fewer dictionary coefficients, an adaptive sparse image partitioning method is introduced. Simulation result demonstrates that the proposed image compression method yields improved encoding efficiency as compared to existing schemes.

Keywords: image compression; intra prediction; dictionary learning; sparse representation; K-SVD.

DOI: 10.1504/IJITCA.2018.092457

International Journal of Internet of Things and Cyber-Assurance, 2018 Vol.1 No.2, pp.137 - 157

Received: 06 May 2017
Accepted: 08 Oct 2017
Published online: 21 Jun 2018 *

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