Hybrid Neural Network Predictive-Wavelet Image Compression System
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1397
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2014.02.078
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 975
- Volume
- 151
- Issue
- 3
- ISSN
- 0925-2312
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 23
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The underpinning research by an UK-UAE collaboration proposes a hybrid system that combines the properties of the predictive and transform coding for the compression of images, removing interpixel redundancy and improving the compression rate by using wavelet coding. Relative to previous techniques, this approach combines ease of implementation with the effective quality and transmission rate of the compressed images. The improvement in compliance with the JPEG 2000 standard proved the potential to commercialise the technique. The work led to a funded keynote address at the 4th IEEE Latin American Conference on Computational Intelligence (LA-CCI 2017), Arequipa, Peru.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
