JPEG and BPG visually lossless image compression via KonJND-1k database
Abstract
Introduction/purpose: This paper presents the results of the research on visually lossless image compression which is of particular interest because it achieves a high degree of compression, while the visual quality of the image is not impaired, i.e., end users are very satisfied with the image quality. The analysis was carried out using the publicly available large-scale picture-wise KonJND-1k database which contains the results of subjective tests on JPEG and BPG compressed images.
Methods: Thanks to the availability of images from the KonJND-1k database, the dependence of objective assessments of image quality on parameters that control the degree of compression of source signals (quality factor for JPEG and quantization parameter for BPG) is analyzed. The results of the visually lossless subjective tests are used for a deep analysis of the boundary and typical values of the parameters that control these two types of compression, as well as for the analysis of the corresponding values of the objective quality scores. Furthermore, reliable features for predicting the boundary between visually lossless and visually lossy compression have been identified. For that purpose, the degree of agreement between the predictions and the ground truth values of the peak signal-to-noise ratio (PSNR) and image representation in bits per pixel (bpp) is used. The visually lossless compression ratio is used to compare JPEG and BPG techniques.
Results: It is shown that the boundary between visually lossless and visually lossy image compression is found in a wide range of PSNR values (about 20 dB for JPEG and 15 dB for BPG). The corresponding JPEG image compression quality factor values at this threshold also range widely from 31 to 79, with concentration between 40 and 45. For the BPG encoder, the values of the quantization parameter are grouped around 30, and the boundary values are 25 and 34. Furthermore, it is shown that this boundary can be reliably determined based on simple features derived from the original uncompressed image. Gradient-based features known as spatial frequency and spatial information proved to be the best predictors. The degree of agreement between the predictions obtained from these features with the ground truth values of PSNR and bpp in both types of compression is greater than 85%. A comparative analysis has showed that, using BPG compression, it is possible, on the average, to achieve a twice larger compression ratio of visually lossless compression than for JPEG (80 versus 40).
Conclusion: Although a high degree of agreement is achieved between the predictions and the ground truth values of PSNR and bpp of the boundary between visually lossless and visually lossy compression, there is a need for the development of new prediction approaches, especially with the BPG technique, which through the compression ratio proved to be superior to the JPEG technique. The existing databases used for the analysis of visually lossless compression contain color images from the visible part of the electromagnetic spectrum. Considering the increasing use of images from the infrared part of the spectrum, there is a need to conduct similar tests in this spectral range.
References
Alakuijala, J., Boukortt, S., Ebrahimi, T., Kliuchnikov, E., Sneyers, J., Upenik, E., Vandevenne, L., Versari, L. & Wassenberg, J. 2020. Benchmarking JPEG XL image compression. In: Schelkens, P. & Kozacki, T. (Eds.) Proceedings SPIE Photonics Europe: Optics, Photonics and Digital Technologies for Imaging Applications VI, Online Only, France, 11353, pp.113530X-1-113530X-20, April 06-10. Available at: https://doi.org/10.1117/12.2556264.
Bellard, F. 2018. BPG Image format. Bellard.org, 21 April [online]. Available at: https://bellard.org/bpg/ [Accessed: 05. April 2024].
Bull, D. & Zhang, F. 2021. Intelligent Image and Video Compression: Communicating Pictures, 2nd Edition. Academic Press. ISBN: 9780128203545.
Bondžulić, B., Stojanović, N., Petrović, V., Pavlović, B. & Miličević, Z. 2021. Efficient Prediction of the First Just Noticeable Difference Point for JPEG Compressed Images. Acta Polytechnica Hungarica, 18(8), pp.201-220. Available at: https://doi.org/10.12700/APH.18.8.2021.8.11.
Bondžulić, B., Bujaković, D., Li, F. & Lukin, V. 2022. On strange images with application to lossy image compression. Radioelectronic and Computer Systems, 104(4), pp.143-152. Available at: https://doi.org/10.32620/reks.2022.4.11.
Bondžulić, B., Pavlović, B., Stojanović, N., Petrović, V. & Bujaković, D. 2024. A simple and reliable approach to providing a visually lossless image compression. The Visual Computer, International Journal of Computer Graphics, 40, pp.3747-3763. Available at: https://doi.org/10.1007/s00371-023-03062-y.
Cai, Y.-Q., Zou, H.-X. & Yuan, F. 2019. Adaptive compression method for underwater images based on perceived quality estimation. Frontiers of Information Technology & Electronic Engineering, 20(5), pp.716-730. Available at: https://doi.org/10.1631/FITEE.1700737.
Corchs, S.E., Ciocca, G., Bricolo, E. & Gasparini, F. 2016. Predicting complexity perception of real world images. PLoS ONE, 11(6), e0157986. Available at: https://doi.org/10.1371/journal.pone.0157986.
Fan, C., Lin, H., Hosu, V., Zhang, Y., Jiang, Q., Hamzaoui, R. & Saupe, D. 2019. SUR-Net: Predicting the satisfied user ratio curve for image compression with deep learning. In: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Berlin, Germany, pp.1-6, June 5-7. Available at: https://doi.org/10.1109/QoMEX.2019.8743204.
Fiorucci, F., Baruffa, G. & Frescura, F. 2012. Objective and subjective quality assessment between JPEG XR with overlap and JPEG 2000. Journal of Visual Communication and Image Representation, 23(6), pp.835-844. Available at: https://doi.org/10.1016/j.jvcir.2012.04.011.
Goerner, F.L., Duong, T., Stafford, R.J. & Clarke, G.D. 2013. A comparison of five standard methods for evaluating image intensity uniformity in partially parallel imaging MRI. Medical Physics, 40(8), art.number:082302. Available at: https://doi.org/10.1118/1.4816306.
Hasler, D. & Suesstrunk, S.E. 2003. Measuring colourfulness in natural images. In: Rogowitz, B.E. & Pappas, T.N. (Eds.) Proceedings Electronic Imaging: Human Vision and Electronic Imaging VIII, Santa Clara, CA, USA, 5007, pp.87-95, January 20-24. Available at: https://doi.org/10.1117/12.477378.
Jin, L., Lin, J.Y., Hu, S., Wang, H., Wang, P., Katsavounidis, I., Aaron, A. & Kuo, C.-C. J. 2016. Statistical study on perceived JPEG image quality via MCL-JCI dataset construction and analysis. In: IS&T International Symposium on Electronic Imaging – Image Quality and System Performance XIII, San Francisco, CA, USA, art.ID:art00026, February 14-18. Available at: https://doi.org/10.2352/ISSN.2470-1173.2016.13.IQSP-222.
Kovalenko, B., Lukin, V., Kryvenko, S., Naumenko, V. & Vozel, B. 2022. Prediction of parameters in optimal operation point for BPG-based lossy compression of noisy images. Ukrainian Journal of Remote Sensing, 9(2), pp.4-12. Available at: https://doi.org/10.36023/ujrs.2022.9.2.212.
Kovalenko, B. & Lukin, V. 2023. Analysis of distortions due to BPG-based lossy compression of noise-free and noisy images. Herald of Khmelnytskyi National University. Technical Sciences, 325(1), pp.128-135.
Krivenko, S.S., Krylova, O., Bataeva, E. & Lukin, V.V. 2018. Smart lossy compression of images based on distortion prediction. Telecommunications and Radio Engineering, 77(17), pp.1535-1554. Available at: https://doi.org/10.1615/TelecomRadEng.v77.i17.40.
Li, F., Lukin, V., Okarma, K. & Fu, Y. 2021. Providing a Desired Quality of BPG Compressed Images for FSIM Metric. In: 2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, pp.10-14, December 15-17. Available at: https://doi.org/10.1109/ATIT54053.2021.9678522.
Li, F., Lukin, V., Ieremeiev, O. & Okarma, K. 2022. Quality control for the BPG lossy compression of three-channel remote sensing images. Remote Sensing, 14(8), art.number:1824. Available at: https://doi.org/10.3390/rs14081824.
Li, F., Abramov, S., Dohtiev, I. & Lukin, V. 2024. Advantages and drawbacks of two-step approach to providing desired parameters in lossy image compression. Advanced Information Systems, 8(1), pp.57-63. Available at: https://doi.org/10.20998/2522-9052.2024.1.07.
Lin, H., Hosu, V., Fan, C., Zhang, Y., Mu, Y., Hamzaoui, R. & Saupe, D. 2020. SUR-FeatNet: Predicting the satisfied user ratio curve for image compression with deep feature learning. Quality and User Experience, 5, art.number:5. Available at: https://doi.org/10.1007/s41233-020-00034-1.
Lin, H., Chen, G., Jenadeleh, M., Hosu, V., Reips, U.-D., Hamzaoui, R. & Saupe, D. 2022. Large-Scale Crowdsourced Subjective Assessment of Picturewise Just Noticeable Difference. IEEE Transactions on Circuits and Systems for Video Technology, 32(9), pp.5859-5873. Available at: https://doi.org/10.1109/TCSVT.2022.3163860.
Liu, X., Chen, Z., Wang, X., Jiang, J. & Kwong, S. 2018. JND-Pano: Database for just noticeable difference of JPEG compressed panoramic images. In: Hong, R., Cheng, W.H., Yamasaki, T., Wang, M. & Ngo, C.W. (Eds.) Advances in Multimedia Information Processing. PCM 2018. Lecture Notes in Computer Science, 11164, pp.458-468. Cham: Springer. Available at: https://doi.org/10.1007/978-3-030-00776-8_42.
Liu, H., Zhang, Y., Zhang, H., Fan, C., Kwong, S., Kuo C.-C. J. & Fan, X. 2020. Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression. IEEE Transactions on Image Processing, 29, pp.641-656. Available at: https://doi.org/10.1109/TIP.2019.2933743.
Liu, Y., Jin, J., Xue, Y. & Lin, W. 2023. The First Comprehensive Dataset with Multiple Distortion Types for Visual Just-Noticeable Differences. In: 2023 IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia, pp.2820-2824, October 08-11. Available at: https://doi.org/10.1109/ICIP49359.2023.10221977.
Mikhailiuk, A., Ye, N. & Mantiuk, R.K. 2021. The effect of display brightness and viewing distance: A dataset for visually lossless image compression. In: IS&T International Symposium on Electronic Imaging: Human Vision and Electronic Imaging, Virtual Event, 33, art.ID:art00005, January 21st. Available at: https://doi.org/10.2352/ISSN.2470-1173.2021.11.HVEI-152.
Mittal, A., Moorty, A.K. & Bovik, A.C. 2012. No-Reference Image Quality Assessment in the Spatial Domain. IEEE Transactions on Image Processing, 21(12), pp.4695-4708. Available at: https://doi.org/10.1109/TIP.2012.2214050.
Pavlović, B., Bondžulić, B., Stojanović, N., Petrović, V. & Bujaković, D. 2023. Prediction of the first just noticeable difference point based on simple image features. In: 2023 Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, Serbia, pp.125-130, May 29-31. Available at: https://doi.org/10.1109/ZINC58345.2023.10173865.
Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J. & Lukin, V. 2007. On between-coefficient contrast masking of DCT basis functions. In: 3rd International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), Scottsdale, Arizona, USA, pp.1-4, January 25-26 [online]. Available at: http://ponomarenko.info/vpqm07_p.pdf [Accessed: 05. April 2024].
Poth, M., Trpovski, Ž. & Lončar-Turukalo, T. 2020. Analysis and improvement of JPEG compression performance using custom quantization and block boundary classifications. Acta Polytechnica Hungarica, 17(6), pp.171-191. Available at: https://doi.org/10.12700/APH.17.6.2020.6.10.
Saha, S. & Vemuri, R. 2000. An analysis on the effect of image features on lossy coding performance. IEEE Signal Processing Letters, 7(5), pp.104-107. Available at: https://doi.org/10.1109/97.841153.
Shen, X., Ni, Z., Yang, W., Zhang, X., Wang, S. & Kwong, S. 2020. A JND Dataset Based on VVC Compressed Images. In: 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), London, United Kingdom, pp.1-6, July 6-10. Available at: https://doi.org/10.1109/ICMEW46912.2020.9105955.
Shen, X., Ni, Z., Yang, W., Zhang, X., Wang, S. & Kwong, S. 2021. Just Noticeable Distortion Profile Inference: A Patch-Level Structural Visibility Learning Approach. IEEE Transactions on Image Processing, 30, pp.26-38. Available at: https://doi.org/10.1109/TIP.2020.3029428.
Testolina, M., Hosu, V., Jenadeleh, M., Lazzarotto, D., Saupe, D. & Ebrahimi T. 2023. JPEG AIC-3 Dataset: Towards Defining the High Quality to Nearly Visually Lossless Quality Range. In: 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), Ghent, Belgium, pp.55-60, June 20-22. Available at: https://doi.org/10.1109/QoMEX58391.2023.10178554.
Wang, Z. & Bovik, A.C. 2009. Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures. IEEE Signal Processing Magazine, 26(1), pp.98-117. Available at: https://doi.org/10.1109/MSP.2008.930649.
Wang, Z., Bovik, A.C., Sheikh, H.R. & Simoncelli, E.P. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), pp.600-612. Available at: https://doi.org/10.1109/TIP.2003.819861.
Yu, H. & Winkler, S. 2013. Image complexity and spatial information. In: 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX), Klagenfurt am Worthersee, Austria, pp.12-17, July 3-5. Available at: https://doi.org/10.1109/QoMEX.2013.6603194.
Zemliachenko, A., Ponomarenko, N., Lukin, V., Egiazarian, K. & Astola, J. 2016. Still image/video frame lossy compression providing a desired visual quality. Multidimensional Systems and Signal Processing, 27(3), pp.697-718. Available at: https://doi.org/10.1007/s11045-015-0333-8.
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