Open AccessMemorability‐based image compression
Author(s) -
Khanna Meera Thapar,
Ralekar Chetan,
Goel Anurika,
Chaudhury Santanu,
Lall Brejesh
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6097
Subject(s) - computer science , image compression , artificial intelligence , convolutional neural network , compression (physics) , data compression , compression artifact , computer vision , image (mathematics) , pattern recognition (psychology) , compression ratio , image processing , materials science , composite material , internal combustion engine , automotive engineering , engineering
This study is concerned with achieving the image compression using the concept of memorability. The authors have used memorability of an image, as a perceptual measure while image coding. In the proposed approach, a region‐of‐interest‐based memorability preserving image compression algorithm which is accomplished via two sub‐processes namely, memorability prediction and image compression is introduced. The memorability of images is predicted using convolutional neural network and restricted Boltzmann machine features. Based on these features, the memorability score of individual patches in an image is calculated and these scores are used to generate the memorability map. These memorability map values are used for optimised image compression. In order to validate the results, an eye tracking experiment with human participants is performed. The comparative analysis shows that the memorability‐based compression outperforms the state‐of‐the‐art compression techniques.