PERFORMANCE ANALYSIS OF VISUALLY LOSSLESS IMAGE COMPRESSION

Transcription

1 PERFORMANCE ANALYSIS OF VISUALLY LOSSLESS IMAGE COMPRESSION Nikolay Ponomarenko (*), Alexander Zemlyachenko (*),Vladimir Lukin (*), Karen Egiazarian (**) and Jaakko Astola (**) (*) National Aerospace University, Kharkov, Ukraine (**) Tampere University of Technology, Tampere, Finland ABSTRACT In many applications, it is desirable to provide visually lossless (but, in fact, lossy) compression of still images. This can be done using modern visual quality metrics and iterative image compression/decompression procedure for setting a proper parameter of a coder for a given image. Performance of such a procedure is analyzed for a wide set of grayscale images and components of color images for several lossy compression techniques, both standard and advanced ones. Results are obtained for two human vision system (HVS) metrics, PSNR-HVS-M and MSSIM and they are in good agreement between each other. It is shown that a provided compression ratio (CR) considerably depends upon complexity of an image subject to lossy compression. The provided CR varies in very wide limits (from 3 to 30 for the used values of the metrics). It is also demonstrated that modern advanced HVS-adapted coders are able to produce by larger CR than standards JPEG and JPEG2000 for the same visual quality. 1. INTRODUCTION Visually lossless compression is important in quite many applications such as medical imaging, remote sensing, digital photography, etc. [1, 2]. The main reasons for using visually lossless (or near-lossless) compression are the following. On one hand, lossless methods of image compression are often unable to provide a desired (large enough) CR especially if an image subject to compression is corrupted by quite intensive noise [3, 4]. On the other hand, there are practical situations where customers of compressed images insist on invisibility of distortions introduced by lossy compression because otherwise images lose their diagnostic or interpretation value. Then, there is a set of requirements to visually lossless methods of image compression. A main of them is that visually lossless compression should be guaranteed and this has to be done in an automatic manner for any compressed image irrespectively to its properties (complexity defined by texture/edge/detail structure and possible noise presence). A second requirement is to produce a larger CR if possible. To ensure invisibility of distortions, one needs to solve a threefold task: (a) to have a proper visual quality metric adequately characterizing image visual quality taking into account HVS, (b) to know a threshold value for this metric for which introduced distortions are still invisible (with high probability) and (c) to provide lossy compression with the value of the selected metric over this threshold [5]. Note that there are several visual quality metrics (indices) such as MSSIM [6], PSNR-HVS- M [7], WSNR [8] and some others that are able to characterize well enough quality of lossy compressed images [9]. Recently, it has been shown for grayscale images [5] that it is enough to provide MSSIM values larger than or PSNR-HVS-M values larger than 40dB in order to guarantee practically invisibility of distortions in lossy compressed images. The procedures for reaching a desired (preset) value of a used quality metric exist. They are iterative and presume image compression/decompression, HVS-metric calculation, its comparison to threshold and simple logical operation at any iteration. The goals of this paper are the following. First, we analyze compression results for a wide set of grayscale images (there were only three test images considered in [5]) including images where noise in original images is visible. Second, we present data for seven coders, all based on orthogonal transforms, including standard coders and recently proposed ones specially designed to provide higher visual quality. 2. METHODOLOGY OF ANALYSIS Analysis has been carried out for 19 images including several conventional ones, their color components and several additional test images. The conventional test images (both grayscale and color components) Baboon, Peppers, Barbara as well as grayscale images Lenna, Boat, Airfield, Goldhill, and Cameraman have been widely used

2 in simulations and algorithm verification by image processing community. However, possibly, not everyone has noticed that color components of color versions of the test images Barbara and Peppers have visually noticeable noise, especially their red and blue components. Besides, we have used two unconventional grayscale test images, Grass and Pole, earlier analyzed in [10]. They are presented below in Fig. 1. The image Grass is highly textural and has high masking effect [11] (similarly to the standard test image Baboon). On the contrary, the image Pole is characterized by a large percentage of pixels that relate to homogeneous image regions and by the presence of high contrast edges. The following seven methods of lossy compression have been studied. First, the standard JPEG with the conventional non-uniform quantization of DCT coefficients [12] has been used. Second, the wavelet based coders JPEG2000 [13] and SPIHT [14] have been considered. The DCT based coders AGU [15] and ADCT [16] (that use 32x32 pixel and adaptive size blocks, respectively) have been exploited. Finally, we exploited versions of the latter two coders adapted to provide better visual quality [17]. To collect the data we perform the following procedure. For a given value of the metric (MSSIM and PSNR-HVS- M have been considered, their values were 0.985, 0.99, and 40, 42, 44 db, respectively), each image has been compressed in a lossy manner to provide a given metrics value with a reasonable accuracy. CR has been determined for each considered coder. Recall that the performance of lossy compression techniques can be analyzed either for a fixed CR (then metrics values are compared), or for a fixed value of the quality metric (then it is possible to compare attained CR values). Here we prefer exploiting the latter approach. This allows comparing coder performance in terms of CR depending upon image complexity. The results for PSNR- HVS-M=40 db are presented in Fig. 2 (with data on the achieved CR values in Table below the plots). CR values for each coder are given over each considered test image. Similarly, Fig. 3 presents data obtained for the metric MSSIM= COMPARATIVE ANALYSIS Analysis of data presented in Figures 2 and 3 as well as in the corresponding Tables shows the following. First, the smallest CRs for all coders are observed for the images with the most complex structure (textural ones) such as Grass and Baboon. The provided CR values are by several times larger for a middle-complexity and simple images. This means that the tendency for the case of using visual quality metrics is the same as for the traditional metrics such as MSE or PSNR. For highly textural images (Grass and Baboon, both grayscale and components of color images), CR values for the coders JPEG2000, SPIHT, AGU, and ADCT are almost the same (although the coders AGU and ADCT provide larger CR than JPEG2000 and SPIHT for given values of the standard metric PSNR [10, 15, 16]). The CR values for JPEG and, especially, AGU-M and ADCT-M are essentially larger. This clearly demonstrates that visual quality has to be paid a special attention in a design of lossy compression techniques. Fig. 1. Test images Grass and Pole

3 Fig. 2. CR values for different test images and used coders with the provided PSNR-HVS-M=40 db (data are also given in Table below) Fig. 3. CR values for different test images and used coders with the provided MSSIM=0.985

4 Fig. 4. CR values for different test images and used coders with the provided PSNR-HVS-M=42 db Fig. 5. CR values for different test images and used coders with the provided MSSIM=0.99

5 Note that for a middle-complexity and simple-structure test images the coders AGU-M and ADCT-M considerably outperform their counterparts AGU and ADCT, respectively, not adapted to improve image visual quality. The benefit can be quite large (consider, e.g., data for the test images Peppers and Pole). The results for JPEG2000 and SPIHT are quite close and they are mostly worse than those for JPEG according to the metric PSNR- HVS-M. In turn, according to the metric MSSIM, the results for JPEG, JPEG2000 and SPIHT are practically the same. These data partly explain why JPEG still successfully competes with JPEG2000 in compression applications where visual quality is of the prime importance. CR values are the largest for the coder ADCT-M for almost all considered test images according to both metrics. AGU-M approaches to ADCT-M for rather complex images but there is a difference in CR for quite simple images. Figures 4 and 5 present the CR values obtained under condition that PSNR-HVS-M=42 db and MSSIM=0.99 are provided, respectively. The observations for these data are the same as given above. The only difference is that the provided CRs are smaller than for PSNR-HVS-M=40 db and MSSIM=0.985 discussed earlier. In general, the data for the considered two metrics are in good agreement. An interesting effect has been observed for components of color images. As it has been already mentioned, noise is visible in red and blue components of such images as Peppers and Barbara although it is practically not noticeable in green component and grayscale (intensity) components. As it is known [18, 19], lossy compression of noisy images possesses specific noise filtering effect that can be, in general, treated as a positive feature. Visual quality metrics used in iterative compression procedure also confirm this effect and perceive it as a visible difference between original and compressed images. Due to this, compression ratios for red and blue components are usually smaller than those for green and, especially, intensity components. This effect is demonstrated in Fig. 6. The original blue component of the color image Barbara is given in Fig. 6,a. Noise is visible especially in homogeneous regions. The image compressed by ADCT-M with the provided PSNR- HVS-M=40 db is represented in Fig. 6,b. As it can be seen, noise is partly suppressed and, because of this, there is a noticeable difference between two images. We have also tested an applicability of visually lossless compression to hyperspectral (namely, Hyperion) images. For CR of the range observed in the case of component-wise compression, no visually noticeable distortions have been observed. Moreover, classification accuracy is practically the same as in the case of original (uncompressed or losslessly compressed) image classification. This means that the approach based on visually lossless compression can be effectively used to compress remote sensing data providing about 5 times larger CR than for lossless compression without any decrease in classification accuracy. a b Fig. 6. Blue component of the color test image Barbara (a) and this image compressed by ADCT-M The number of iterations depends on an algorithm applied and accuracy of providing a desired value of used metric. Usually less than ten iterations are needed. The studies carried out have allowed accelerating visually lossless

6 compression by reducing the number of iterations applying the proposed recommendations on the parameter setting (starting quantization step or bpp) of the designed iterative procedures. In particular, acceleration can be achieved by setting an initial quantization step for DCT based coders as QS=(I max -I min )/25, where I max and I min are range of data representation for a given image. 4. CONCLUSIONS AND FUTURE WORK Analysis of the coder performance for visually lossless compression mode is carried out. It is shown that the attained CR considerably depends upon complexity of a compressed image. It is possible to increase CR by exploiting advanced coders adapted to HVS (for simple structure images by up to 1.5 times). In the future, we plan to study the coder performance using just noticeable difference metrics. 5. REFERENCES [1] A. Fidler, U. Skaleric, B. Likar, The impact of image information on compressability and degradation in medical image compression, Med. Phys. 33(8), pp , August [2] B. Aiazzi, L. Alparone, S. Baronti, Near-lossless compression of 3-D optical data, IEEE Transactions on Geoscience and Remote Sensing, vol. 39, No 11, pp , Nov [3] A. Kaarna, Compression of Spectral Images, Vision Systems: Segmentation and Pattern Recognition, Vienna: I-Tech, Vol. 1, pp , [4] Bovik, A. Handbook of Image and Video Processing, Academic Press, USA, 1429 p., [5] 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, pp , October [6] Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," Invited Paper, IEEE Asilomar Conference on Signals, Systems and Computers, Nov [7] N. Ponomarenko, F. Silvestri, K. Egiazarian, M. Carli, J. Astola, V. Lukin, "On between-coefficient contrast masking of DCT basis functions", CD-ROM Proc. of the Third International Workshop on VPQM, 4 p., [8] D.M. Chandler, S.S. Hemami, VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images, IEEE Transactions on Image Processing, Vol. 16 (9), pp , [9] 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, Vol. 6, 6 p., [10] N. Ponomarenko, V. Lukin, K. Egiazarian, E. Delp, Comparison of lossy compression technique performance for real life color photo images, Proceedings of Picture Coding Symposium, USA, 4 p., June [11] US Patent 5,426,512 USA. Image data compression having minimum perceptual error, A. B. Watson; [12] JPEG. The JPEG committee home page: [13] Taubman D., Marcellin M. - Standards and Practice,Boston: Kluwer, JPEG 2000: Image Compression Fundamentals, Boston: Kluwer, [14] A. Said, W.A. Pearlman, A new fast and efficient image codec based on set partitioning in hierarchical trees, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 6, pp , [15] N.N. Ponomarenko, V.V. Lukin, K. Egiazarian, J. Astola, DCT Based High Quality Image Compression, Proceedings of 14th Scandinavian Conference on Image Analysis, Joensuu, Finland, Vol. 14, pp , June [16] N. Ponomarenko, V. Lukin, K. Egiazarian, J. Astola, ADCT: A new high quality DCT based coder for lossy image compression, CD ROM Proceedings of LNLA, Switzerland, 6 p., [17] N. Ponomarenko, S. Krivenko, V. Lukin, K. Egiazarian, Visual Quality of Lossy Compressed Images, Proceedings of CADSM 2009, Svalyava, Ukraine, pp , Feb [18] Al-Chaykh O.K., Mersereau R.M., Lossy compression of noisy images, IEEE Transactions on Image Processing, Vol. 7 (12), pp , [19] N. Ponomarenko, S. Krivenko, V. Lukin, K. Egiazarian, Lossy Compression of Noisy Images Based on Visual Quality: A Comprehensive Study, EURASIP Journal on Advances in Signal Processing, 13 p., 2010.