Int. J. Internet of Things and Cyber-Assurance, Vol. 1, No. 2, 2018 137 Copyright © 2018 Inderscience Enterprises Ltd. Dictionary-based intra-prediction framework for image compression via sparse representation Arabinda Sahoo* and Pranati Das Department of ECE, ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, 751030, India and Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, 759145, India Email: arubabu123@yahoo.co.in Email: daspranati@yahoo.co.in *Corresponding author 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. Reference to this paper should be made as follows: Sahoo, A. and Das, P. (2018) ‘Dictionary-based intra-prediction framework for image compression via sparse representation’, Int. J. Internet of Things and Cyber-Assurance, Vol. 1, No. 2, pp.137–157. Biographical notes: Arabinda Sahoo is currently working as an Assistant Professor in the Department of ECE at the ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India. His areas of research interest include signal and image processing, image compression, and sparse representation. Pranati Das is currently working as an Associate Professor in the Department of EE at the IGIT, Sarang, Odisha, India. She has published a number of research and conference papers in both national and international forum. Her area of research interests are signal processing and image processing.