Using online dictionary learning to improve Bayer pattern image coding
by Tingyi Zheng; Li Wang
International Journal of Computational Science and Engineering (IJCSE), Vol. 16, No. 2, 2018

Abstract: Image quality is a fundamental concern in image compression. There is a lot of noise in image compression process, which may impact on users not getting precise identification. It has, thus, always been neglected in image compression in the past researches. In fact, noise takes a beneficial role in image reconstruction. In this paper, we chose noise as considered and recommended as a coding method for Bayer pattern image based on online dictionary learning. Investigations have depicted that the proposed method in Bayer pattern image coding might develop the rate of distortion performance of Bayer pattern image coding at any rate.

Online publication date: Mon, 19-Mar-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com