Abstract
Lossless image compression techniques play a crucial role in preserving image quality while reducing storage space and transmission bandwidth. This paper proposes a novel hybrid integrated method for lossless image compression by combining Contrast Limited Adaptive Histogram Equalization (CLAHE), two-channel encoding, and adaptive arithmetic coding to achieve highly efficient compression without any loss of image information. The first step of the proposed approach involves applying Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the local contrast of the image. This pre-processing step aids in reducing the entropy and increasing the redundancy in the image, creating a more favourable environment for subsequent compression algorithms. Next, the image is divided into two channels: one channel focuses on encoding essential structural information, while the other channel handles the finer details. This segregation leverages the inherent properties of images to improve compression efficiency. To achieve further compression gains, an adaptive arithmetic coding algorithm for encoding the data in each channel is utilized. Adaptive arithmetic coding adapts its probability model during the encoding process, leading to improved compression performance compared to traditional static coding methods. The proposed method offers significant potential in various applications, it is especially crucial in medical imaging, where large volumes of high-resolution images are generated during procedures such as MRI, CT scans, or digital pathology, transmitting high-quality images in resource-constrained environments, and facilitating image processing tasks requiring precise data preservation. CLAHE can be a valuable tool in medical imaging to enhance essential diagnostic information in medical images before compression. By improving contrast and visibility of structures, CLAHE may aid in achieving better compression efficiency and reduce the risk of introducing compression artifacts. To assess the effectiveness of our proposed method, comprehensive experiments are conducted on various benchmark image datasets. The performance evaluation parameters such as compressed image size, compression ratio, coding efficiency, compression gain and bit rate evaluated. The results demonstrate that the proposed approach achieves superior compression ratios while ensuring lossless reconstruction of the original image. The incorporation of CLAHE enhances the compression efficiency by exploiting local image characteristics, while two-channel encoding and adaptive arithmetic coding work synergistically to achieve high compression gains.
Similar content being viewed by others
Data Availability
The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.
References
Bovik AC. Handbook of image and video processing. Cambridge: Academic Press; 2010.
Taubman D, Marcellin M. JPEG2000 image compression fundamentals, standards and practice, vol. 642. New York: Springer Science & Business Media; 2012. p. 773.
Raghavendra C, Sivasubramanian S, Kumaravel A. Improved image compression using effective lossless compression technique. Cluster Comput. 2019. https://doi.org/10.1007/s10586-018-2508-1.
Kim Y, et al. Towards the perceptual quality enhancement of low bit-rate compressed images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020.
Rahman MA, Hamada M. Lossless image compression techniques: a state-of-the-art survey. Symmetry. 2019;11:1274. https://doi.org/10.3390/sym11101274.
Manju RA, Koshy G, Simon P. Improved method for enhancing dark images based on CLAHE and morphological reconstruction. Proc Comput Sci. 2019;165:391–8.
Musa P, Rafi F, Lamsani M. A review: contrast-limited adaptive histogram equalization (CLAHE) methods to help the application of face recognition. 2018; 1–6. https://doi.org/10.1109/IAC.2018.8780492.
Koonsanit K et al. Image enhancement on digital x-ray images using N-CLAHE. In: 2017 10th Biomedical Engineering International Conference (BMEICON). IEEE, 2017.
Koff D, et al. Implementing a large-scale multicentric study for evaluation of lossy JPEG and JPEG2000 medical image compression: challenges and rewards. In: Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment. Vol. 6515. SPIE, 2007.
Cucchiara R, Piccardi M, Prati A. Exploiting cache in multimedia. In: Proceedings IEEE International Conference on multimedia computing and systems. 1999;Vol. 1. IEEE.
Sharma U, Sood M, Puthooran E. Lossless compression of medical image sequences using a resolution independent predictor and block adaptive encoding. Int J Electr Comput Eng Syst. 2018;9(2):69–79.
Salem Nema, et al. Medical image enhancement based on histogram algorithms. Procedia Computer Science. 2019;163(163):300–11. https://doi.org/10.1016/j.procs.2019.12.112.
Sharma U, et al. A block-based arithmetic entropy encoding scheme for medical images. In: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, edited by Information Resources Management Association, IGI Global, 2023:190–206. https://doi.org/10.4018/978-1-6684-7544-7.ch012.
Rani M, Rao G, Rao B. Retraction Note to: An efficient codebook generation using firefly algorithm for optimum medical image compression. J Ambient Intell Humaniz Comput. 2022. https://doi.org/10.1007/s12652-022-04313-x.
Rani SJ, Glorindal G, Herman IA. Medical image compression using DCT with entropy encoding and huffman on MRI brain images. Asian J Appl Sci Technol (AJAST). 2022;6(2):16–25.
Sharma U, Sood M, Puthooran E. A block adaptive near-lossless compression algorithm for medical image sequences and diagnostic quality assessment. J Digit Imaging. 2020;33(2):516–30.
Rojas-Hernández R, et al. Lossless medical image compression by using difference transform. Entropy. 2022;24(7):951.
Salih HM, Kadhim AM. Medical image compression based on SPIHT-BAT algorithms. IOP Conf Ser Mater Sci Eng. 2021;1076(1):1–18.
Wu X, Memon N. Context-based, adaptive, lossless image coding. IEEE Trans Commun. 1997;45(4):437–44.
Blelloch G. Introduction to data compression. Carnegie Mellon University; 2010.
Salomon D. Data compression: the complete reference. Springer; 2007.
Marpe D, et al. Video compression using context-based adaptive arithmetic coding. In: Proceedings 2001 International Conference on Image Processing (Cat. No. 01CH37205). Vol. 3. IEEE, 2001.
Sulthana, Nigar S, Chandra M. Image compression with adaptive arithmetic coding. Int J Comput Appl. 2010;1(18):31–4.
Taubman D. Software architectures for JPEG2000. In: 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No. 02TH8628). Vol. 1. IEEE, 2002.
Hidayat T, Zakaria MH, NaimChe Pee A. Survey of performance measurement indicators for lossless compression technique based on the objectives. In: 2020 3rd International Conference on information and communications technology (ICOIACT). IEEE, 2020.
Dhawan S. A review of image compression and comparison of its algorithms. Int J Electron Commun Technol. 2011;2(1):22–6.
Avudaiappan T, et.al. Performance analysis on lossless image compression techniques for general images, Int J Pure and Appl Math. 2017;117(10):1–5. https://doi.org/10.12732/ijpam.v117i10.1.
Mahendiran N, Deepa C. A comprehensive analysis on image encryption and compression techniques with the assessment of performance evaluation metrics. SN Comput Sci. 2021;2(1):29.
Parithiet I, et al. Review on different lossless image compression techniques. Int J Mod Sci Eng Technol. 2015;2(4):86–94 (ISSN 2349-3755).
Poornima P, Nithya V. Performance evaluation of lossy image compression techniques based on the image profile. In: Computational signal processing and analysis: select Proceedings of ICNETS2, Volume I. Springer Singapore, 2018.
Garg G, Kumar R. Analysis of image types, compression techniques and performance assessment metrics: a review. J Inf Optim Sci. 2022;43(3):429–36.
Kavitha S, Anandhi RJ. A survey of image compression methods for low depth-of-field images and image sequences. Multimed Tools Appl. 2015;74:7943–56.
Acknowledgements
The authors acknowledged the REVA University, Bangalore, India for supporting the research work by providing the facilities.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Contributions
Under Dr M. Prabhakar supervision, Mr.P R Rajesh Kumar identified research problems, performed analysis, and authored the paper. Additionally, he conducted simulations and analyzed the obtained results.
Corresponding author
Ethics declarations
Conflict of interest
No conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kumar, P.R.R., Prabhakar, M. An Integrated Approach for Lossless Image Compression Using CLAHE, Two-Channel Encoding and Adaptive Arithmetic Coding. SN COMPUT. SCI. 5, 523 (2024). https://doi.org/10.1007/s42979-024-02866-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-024-02866-6