Analysis of the potential efficiency of post-filtering noisy images after lossy compression
Keywords:grayscale images, lossy image compression, optimal operation point, BPG coder, post-filtering, DCT-based filter
An increase in the number of images and their average size is the general trend nowadays. This increase leads to certain problems with data storage and transfer via communication lines. A common way to solve this problem is to apply lossy compression that provides sufficiently larger compression ratios compared to lossless compression approaches. However, lossy compression has several peculiarities, especially if a compressed image is corrupted by quite intensive noise. First, a specific noise-filtering effect is observed. Second, an optimal operational point (OOP) might exist where the quality of a compressed image is closer to the corresponding noise-free image than the quality of the original image according to a chosen quality metric. In this case, it is worth compressing this image in the OOP or its closest neighborhood. These peculiarities have been earlier studied and their positive impact on image quality improvement has been demonstrated. Filtering of noisy images due to lossy compression is not perfect. Because of this, it is worth checking can additional quality improvement be reached using such an approach as post-filtering. In this study, we attempt to answer the questions: “is it worth to post-filter an image after lossy compression, especially in OOP’s neighborhood? And what benefit can it bring in the sense of image quality?”. The study is carried out for better portable graphics (BPG) coder and the DCT-based filter focusing mainly on one-component (grayscale) images. The quality of images is characterized by several metrics such as PSNR, PSNR-HVS-M, and FSIM. Possible image quality increasing via post-filtering is demonstrated and the recommendations for filter parameter setting are given.
Bazi, Y., Cavallaro, G., Demir, B., Melgani, F. (2022). Learning from Data for Remote Sensing Image Analysis. International Journal of Remote Sensing, 43(15-16), 5527–5533. DOI: 10.1080/01431161.2022.2131481.
Bellard, F. (2018). BPG Image format. Retrieved from: https://bellard.org/bpg/.
Blanes, I., Magli, E., Serra-Sagrista, J. (2014) A Tutorial on Image Compression for Optical Space Imaging Systems. IEEE Geoscience and Remote Sensing Magazine, 2(3), 8–26. DOI: 10.1109/MGRS.2014.2352465.
Chatterjee, P., Milanfar, P. (2010). Is Denoising Dead? IEEE Transactions on Image Processing, 19(4), 895–911. DOI: 10.1109/TIP.2009.2037087.
Christophe, E. (2011). Hyperspectral Data Compression Tradeoff. Prasad, S. et al. (eds). Optical Remote Sensing. Augmented Vision and Reality, 3. DOI: 10.1007/978-3-642-14212-3_2.
Colom, M., Buades, A., Morel, J.-M. (2014). Nonparametric noise estimation method for raw images. J. Opt. Soc. Am., 31(4), 863–871. DOI: 10.1364/JOSAA.31.000863.
Fevralev, D., Lukin, V., Ponomarenko, N., Abramov, S., Egiazarian, K., Astola, J. (2011) Efficiency analysis of DCT-based filters for color image database. SPIE Conference Image Processing: Algorithms and Systems VII, 7870.
Foi, A. (2007). Pointwise Shape-Adaptive DCT Image Filtering and Signal-Dependent Noise Estimation : Thesis for the degree of Doctor of Technology. Tampere University of Technology, Tampere, Finland, 194.
Hussain, A. J., Al-Fayadh, A., Radi, N. (2018). Image compression techniques: A survey in lossless and lossy algorithms. Neurocomputing, 300, 44–69.
Kovalenko, B., Lukin, V. (2022). Usage of different Chroma Subsampling Modes in Image Compression by BPG Coder. Ukrainian Journal of Remote Sensing, 9(3), 11–16. DOI: 10.36023/ujrs.2022.9.3.216.
Kovalenko, B., Lukin, V., Kryvenko, S., Naumenko, V., Vozel, B. (2022). BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters. Applied Sciences, 12(15), 7555. DOI: 10.3390/app12157555.
Kovalenko, B., Lukin, V., Naumenko, V., Krivenko, S. (2021). Analysis of noisy image lossy compression by BPG using visual quality metrics. IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT), 20–25.
Lukin, V., Abramov, S., Ponomarenko, N., Egiazarian, K., Astola, J. (2011). Image Filtering: Potential Efficiency and Current Problems. Proceedings of ICASSP, 4.
Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R., Zomaya, A. & Jie, W. (2015). Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems, 51, 47–60. DOI: 10.1016/j.future.2014.10.029.
Mehmood, M., Shahzad, A., Zafar, B., Shabbir, A., Ali, N. (2022). Remote Sensing Image Classification: A Comprehensive Review and Applications. Mathematical Problems in Engineering, 2022(5880959), 24. DOI: 10.1155/2022/5880959
Penna, B., Tillo, T., Magli, E., Olmo, G. (2007). Transform coding techniques for lossy hyperspectral data compression. IEEE Transactions on Geoscience Remote Sensing, 45(5), 1408–1421. DOI: 10.1109/TGRS.2007.894565.
Ponomarenko, N., Lukin, V., Egiazarian, K. (2011). HVS-metric-based performance analysis of image denoising algorithms. 3rd European Workshop on Visual Information Processing, 156–161. DOI: 10.1109/EuVIP.2011.6045554.
Ponomarenko, N., Lukin, V., Zriakhov, M., Egiazarian, K. (2005). Lossy compression of images with additive noise. Proceedings of International Conference on Advanced Concepts for Intelligent Vision Systems, 3708(2005), 381–386.
Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J., Lukin, V. (2007). On Between-Coefficient Contrast Masking of DCT Basis Functions. In Proceedings of the Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics, 4.
Schowengerdt, R. A. (2007). Remote Sensing: Models and Methods for Image Processing. 3rd ed.; Academic Press: San Diego, CA, USA.
Selva, E., Kountouris, A., Louet, Y. (2021). K-Means Based Blind Noise Variance Estimation. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 1–7. DOI: 10.1109/VTC2021-Spring51267.2021.9449072.
Simmer, K. U., Bitzer, J., Marro, C. (2001). Post-Filtering Techniques. In: Brandstein, M., Ward, D. (eds) Microphone Arrays. Digital Signal Processing. DOI: 10.1007/978-3-662-04619-7_3.
Zhang, L., Zhang, L., Mou, X., Zhang, D. (2011). FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Transactions on Image Processing, 20(8), 2378–2386. DOI: 10.1109/TIP.2011.2109730
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