A compressive sensing approach to perceptual image coding

Mark R. Pickering; Junyong You; Touradj Ebrahimi; Andrew Perkis

doi:10.1117/12.863466

7 September 2010 A compressive sensing approach to perceptual image coding

Mark R. Pickering, Junyong You, Touradj Ebrahimi, Andrew Perkis

Proceedings Volume 7798, Applications of Digital Image Processing XXXIII; 77981E (2010) https://doi.org/10.1117/12.863466
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

Abstract

There exist limitations in the human visual system (HVS) which allow images and video to be reconstructed using fewer bits for the same perceived image quality. In this paper we will review the basis of spatial masking at edges and show a new method for generating a just-noticeable distortion (JND) threshold. This JND threshold is then used in a spatial noise shaping algorithm using a compressive sensing technique to provide a perceptual coding approach for JPEG2000 coding of images. Results of subjective tests show that the new spatial noise shaping framework can provide significant savings in bit-rate compared to the standard approach. The algorithm also allows much more precise control of distortion than existing spatial domain techniques and is fully compliant with part 1 of the JPEG2000 standard.

Citation Download Citation

Mark R. Pickering, Junyong You, Touradj Ebrahimi, and Andrew Perkis "A compressive sensing approach to perceptual image coding", Proc. SPIE 7798, Applications of Digital Image Processing XXXIII, 77981E (7 September 2010); https://doi.org/10.1117/12.863466

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available