A Natural Image Compression Approach Based on Independent Component Analysis and Visual Saliency Detection
In this paper, a natural image compression method is proposed based on independent component analysis (ICA) and visual saliency detection. The proposed compression method learns basis functions trained from data using ICA to transform the image at first; and then sets percentage of
the zero coefficient number in the total transforming coefficients. After that, transforming coefficients are sparser which indicates further improving of compression ratio. Next, the compression method performance is compared with the discrete cosine transform (DCT). Evaluation through both
the usual PSNR and Structural Similarity Index (SSIM) measurements showed that proposed compression method is more robust than DCT. And finally, we proposed a visual saliency detection method to detect automatically the important region of image which is not or lowly compressed while the other
regions are highly compressed. Experiment shows that the method can guarantee the quality of important region effectively.
Keywords: COMPRESSION; DCT; ICA; VISUAL SALIENCY DETECTION
Document Type: Research Article
Publication date: 01 March 2012
- ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content