PERFORMANCE ANALYSIS OF VISUALLY LOSSLESS IMAGE COMPRESSION

1. PERFORMANCE ANALYSIS OF VISUALLY LOSSLESS IMAGE COMPRESSION Vladimir V. Lukin (*), Nikolay N. Ponomarenko (*), Alexander N. Zemlyachenko (*), Karen O. Egiazarian (**), Jaakko T. Astola (**) (*) National Aerospace University, Kharkov, Ukraine (**) Tampere University of Technology, Tampere, Finland Vladimir Lukin San Jose 2008

2. Outline • Applications of visually lossless compression • Approaches to visually lossless compression • Methodology and goal of performance analysis • Test image set • Compared coders • Used metrics of visual quality • Software used • Experiments with PSNR-HVS-M • Experiments with MSSIM • Examples for single-channel and hyperspectral data • Summary and Conclusions Vladimir Lukin San Jose 2008

3. Applications and problems of visually lossless compression • medical imaging, • remote sensing, • digital photography, • Internet and multimedia Reasons for using visually lossless compression: • does not degrade image visual quality; • provides compression ratio (CR) sufficiently larger than for lossless compression. • Problems: • What coders to use? • How to provide visually lossless compression? Vladimir Lukin San Jose 2008

4. Methodology and goal of performance analysis Goal: To compare performance of different coders for a wide set of test images for two visual quality metrics and several threshold values of these metrics. Background for analysis It has been shown recently* that distortions are practically invisible if: PSNR-HVS-M is larger than 40…42 dB; Or MSSIM is larger than 0.985…0.990. *V. Lukin, M. Zriakhov, S. Krivenko, N. Ponomarenko, Z. Miao, Lossy compression of images without visible distortions and its applications, Proceedings of ICSP 2010, Beijing, October, 2010, pp. 694-697. Vladimir Lukin San Jose 2008

5. Test image set • Standard grayscale images: Baboon, Goldhill, Boat, Barbara, Cameraman, Peppers, Lena; • Color components of the standard color images Baboon, Barbara, Peppers (noise in components, especially blue and red, is more seen than in intensity (grayscale) images; • Additional images Grass, Airfield, and Pole • Dimensions: 512x512 pixels, 256 level grayscale Vladimir Lukin San Jose 2008

6. Analyzed and Compared Coders Wavelet based: JPEG2000, SPIHT DCT based JPEG (with non-uniform quantization) AGU (32x32 pixel blocks, uniform quantization) ADCT (with optimized partition scheme, uniform quantization) AGU-M and ADCT-M* (that have non-uniform quantization of DCT coefficients adjusted to HVS) *N. Ponomarenko, S. Krivenko, V. Lukin, K. Egiazarian, Visual Quality of Lossy Compressed Images, Proceedings of CADSM2009, Svalyava, Ukraine, Feb. 2009, pp. 137-142. Vladimir Lukin San Jose 2008

7. Used metrics of visual quality According to recent studies*, these two HVS-metrics are among the best for characterizing visual quality of compressed images. There are established thresholds for distinguishing the cases of visible and invisible distortions. *K. Egiazarian, J. Astola, N. Ponomarenko, V. Lukin, F. Battisti, Metrics Performance Comparison for Color Image Database, Proceedings of the Second International Workshop on Video Processing and Quality Metrics, USA, 2009, Vol. 6, 6 p. Vladimir Lukin San Jose 2008

8. Software Used • The designed software allows: • To compress a given image; • To select a coder; • To chose a metric and its value to be provided; • To set a maximal error the chosen metric is to be provided with; • To display the original and compressed images for visual control. 6) To determine parameters of compression (CR or bpp) and number of iterations required; 7) To change an initial range of compression parameters (quantization step, bpp). Vladimir Lukin San Jose 2008

9. Experiments with PSNR-HVS-M Observation 1: the provided CR values for different test images vary a lot (from about 3 for highly textural images and wavelet based coders till about 30 for simple structure images and DCT-based coders adapted to HVS) Observation 2: JPEG is not worse than JPEG2000 and SPIHT for most images CR values for different test images and used coders with the provided PSNR-HVS-M=40 dB (data are also given in Table below) Vladimir Lukin San Jose 2008

10. Experiments with PSNR-HVS-M Observation 3: The DCT-based coders adapted to HVS (AGU-M and ADCT-M) provide considerably larger CR than other coders for most images, the CR improvement compared to JPEG and JPEG2000 can be up to 1.6 times Observation 4: For larger PSNR-HVS-M (visual quality improves), CR becomes smaller CR values for different test images and used coders with the provided PSNR-HVS-M=42 dB (data are also given in Table below) Vladimir Lukin San Jose 2008

11. Experiments with MSSIM Observation 1: The obtained values of CR values slightly larger than for the case of PSNR-HVS-M=40 dB, the observed tendencies are practically the same Observation 2: JPEG with non-uniform quantization is not worse than JPEG2000 and SPIHT for most images CR values for different test images and used coders with the provided MSSIM=0.985 (data are also given in Table below) Vladimir Lukin San Jose 2008

12. Experiments with MSSIM Observation 1: The obtained values of CR values are similar to the case of PSNR-HVS-M=40 dB, the observed tendencies are also practically the same Observation 2: CR for components of color images are usually smaller than for grayscale (intensity) image CR values for different test images and used coders with the provided MSSIM=0.99 (data are also given in Table below) Vladimir Lukin San Jose 2008

13. Example of component image compression a b Blue component of the color test image Barbara (a) and this image compressed by ADCT-M with PSNR-HVS-M=40 dB (CR=10.76) Some noise suppression is observed; because of this the difference between original and compressed images can be noticed, but visual quality of the compressed image is high Vladimir Lukin San Jose 2008

14. Example of hyperspectral image compression (component-wise) a b CR depending upon sub-band index (n=1,…224) for AVIRIS hyperspectral image for lossless coder ZIP (a) and the coder AGU-M with providing PSNR-HVS-M=40 dB. The designed coders allow compressing hyperspectral data by 5…30 times (depending upon used metric and its value). This is 3…15 times larger than for lossless compression techniques with ensuring practically the same classification accuracy. Vladimir Lukin San Jose 2008

15. Summary and Conclusions • Summary • Analysis of the coder performance for visually lossless compression mode is carried out. • The attained CR considerably depends upon complexity of a compressed image. • It is possible to provide invisibility of introduced distortions automatically. • Conclusions • If one uses the metrics PSNR-HVS-M and MSSIM, the obtained tendencies are in good agreement. • The DCT based coders adapted to HVS provide essentially larger CR than JPEG and JPEG2000 for the same visual quality. • Sometimes visibility of introduced distortions can be due to the presence of noise in original image and its filtering by lossy compression. • The proposed approach allows increasing CR by 3…15 times compared to lossless compression. This can be important for many practical applications. Vladimir Lukin San Jose 2008