Keywords: Image compression, Anisotropic diffusion, Information content, Image enhancement
Abstract: Anisotropic diffusion was introduced in image processing as an image enhancement method. It is a non-linear smoothing process which intends to remove irrelevant or false details while preserving the edges, i.e. it "extracts" the essential visual information. The paper proposes a useful application of anisotropic diffusion in image data compression. We tried to show that for high compression a preliminary anisotropic diffusion on the image yields a better result after coding. This is an important achievement in view of low bit rate transmission.
Figure 1 The original image Clown |
Figure 2 The result of the Perona-Malik diffusion | ||
|
|
| |
Figure 3 The original test image. | Figure 4 Noisy image No.1. Gaussian noise, SNR= 10dB. | Figure 5 Noisy image No.2. Salt and pepper noise, 15% affected pixels. |
Table Compression results for the test image with and
without preprocessing. Compression ratios indicated are the
same for all images within the same rows.
No preprocessing (baseline JPEG) | Preproc. with Perona-Malik diffusion | Preproc. with CNN diffusion | Preproc. with Pure an. diff. (Alv.-L.-Mor.) | |
Noisy image No.1 Compr. 9.7:1 (10.3%) | PSNR=14.34dB |
PSNR=25.41dB |
PSNR=20.13dB |
PSNR=22.33dB |
Noisy image No.2 Compr. 12.5:1 (8%) |
PSNR=15.83dB |
PSNR=25.03dB |
PSNR=19.64dB |
PSNR=23.83dB |
Figure 6 Comparisons of PSNRs for compressions of Clown
with and without preprocessing.
Degree of compression is 100% for uncompressed images. PSNR is calculated versus the image
indicated. AD denotes the anisotropic diffusion.
Figure 7 Detail of the encoded original (compression 7%) |
Figure 8 Detail of the encoded Clown with Perona-M. diff. preprocessing (compression 7%) |