A Flexible and Simple Lossless DWT Filter Bank Using a MAXFLAT FIR Half-Band Filter
Source abstract
Abstract
This paper describes a simple, lossless and computationally efficient two-band single (s-) filter bank that creates an opposite band output by subtracting the primary filtered data from the original data. For computationally efficient and error-free s-filter bank achievement, a maximally flat (MAXFLAT) half-band filter with zero odd-order coefficients is characterized from a unique perfect reconstruction condition, and an explicit impulse–response formula (for non-zero integer coefficients of even order) is derived in a closed form of the filter. The examples are shown to provide a complete and accurate solution for the design of such s-filter banks. In addition, the effectiveness of the proposed s-filter banks is clearly verified by comparing the lossless 5/3 and lossy 9/7 filter banks (in the JPEG2000). The simulation results show that the s-filter banks lead to better performance than the JPEG2000 filter banks using two filters although allowing low computational complexity of less than 50%. This new approach is shown to provide significant advantages over existing lossless discrete wavelet transform (DWT) filter banks in both design flexibility and computational complexity.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Filter Design and Implementation · Advanced Data Compression Techniques · Image and Signal Denoising Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Electronics & Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea
Funding
Korea Institute of Energy Technology Evaluation and Planning · Korea Evaluation Institute of Industrial Technology
References (32)
Show source-provided references
1. Developing a compression procedure based on the wavelet denoising and JPEG2000 compression10.1016/j.ijleo.2020.164933 ↗
2. Remote-sensing image scene classification with deep neural networks in JPEG 2000 compressed domain10.1109/TGRS.2020.3007523 ↗
3. A progressive content-based image retrieval in JPEG 2000 compressed remote sensing archives10.1109/TGRS.2020.2969374 ↗
4. An efficient JPEG-2000 based multimodal compression scheme10.1007/s11042-021-10776-5 ↗
5. Rahman, M.A., Hamada, M., and Shin, J. (2021). The impact of state-of-the-art techniques for lossless still image compression. Electronics, 10.10.3390/electronics10030360 ↗
6. Chervyakov, N., Lyakhov, P., Kaplun, D., Butusov, D., and Nagornov, N. (2018). Analysis of the quantization noise in discrete wavelet transform filters for image processing. Electronics, 7.10.3390/electronics7080135 ↗
7. Chervyakov, N., Lyakhov, P., and Nagornov, N. (2020). Analysis of the quantization noise in discrete wavelet transform filters for 3D medical imaging. Appl. Sci., 10.10.3390/app10041223 ↗
8. Integration of Spatial and Frequency Domain Encryption for Digital Images10.1109/ACCESS.2021.3125961 ↗
9. Two-level wavelet-based convolutional neural network for image deblurring10.1109/ACCESS.2021.3067055 ↗
10. A high speed and memory efficient algorithm for perceptually-lossless volumetric medical image compression
11. Designing digital filter banks using wavelets10.1186/s13634-019-0632-6 ↗
12. A theory of multirate filter banks10.1109/TASSP.1987.1165137 ↗
13. Wavelets and filter banks: Theory and design10.1109/78.157221 ↗
14. Vaidyanathan, P.P. (1993). Multirate Systems and Filter Banks, Prentice Hall.
15. Vetterli, M., and Kovačević, J. (1995). Wavelets and Subband Coding, Prentice Hall PTR.
16. Filters for distortion-free two-band multirate filter banks10.1109/TASSP.1985.1164587 ↗
17. Balasingham, I., and Ramstad, T.A. (1997, January 2–5). On the relevance of the regularity constraint in subband image coding. Proceedings of the IEEE Conference Record of the 31st Asilomar Conference Signals, Systems and Computers, Pacific Grove, CA, USA.
18. Eigenfilter approach to the design of one-dimensional and multidimensional two-channel linear-phase FIR perfect reconstruction filter banks10.1109/TCSI.2008.925818 ↗
19. Exact reconstruction techniques for tree-structured subband coders10.1109/TASSP.1986.1164832 ↗
20. Lattice structures for optimal design and robust implementation of two-channel perfect-reconstruction QMF banks10.1109/29.1491 ↗
21. Filter banks allowing perfect reconstruction10.1016/0165-1684(86)90101-5 ↗
22. Two-channel perfect-reconstruction FIR QMF structures which yield linear-phase analysis and synthesis filters10.1109/29.17560 ↗
23. Analysis of Signals via Non-Maximally Decimated Non-Uniform Filter Banks10.1109/TCSI.2019.2914302 ↗
24. New linear phase QMF filter design for sub-band coding10.1049/el:19910201 ↗
25. General analysis of two-band QMF banks10.1109/78.348138 ↗
26. Horng, B.R., and Wilson, A.N. (1990, January 3–6). Lagrange multiplier approaches to the design of two-channel perfect-reconstruction linear-phase FIR filter banks. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Albuquerque, NM, USA.
27. On the design of FIR wavelet filter banks using factorization of a halfband polynomial10.1109/LSP.2008.922295 ↗
28. The JPEG 2000 still image coding system: An overview10.1109/30.920468 ↗
29. Design of FIR half-band filter with controllable transition-band steepness10.1109/ACCESS.2021.3069985 ↗
30. Design of nonrecursive FIR filters with simultaneously MAXFLAT magnitude and prescribed cutoff frequency10.1016/j.dsp.2012.06.006 ↗
31. Moir, T.J. (2022). FIR Filter Design. Rudiments of Signal Processing and Systems, Springer.10.1007/978-3-030-76947-5 ↗
32. Adams, M.D., and Ward, R.K. (2004, January 17–21). JasPer: A portable flexible open-source software tool kit for image coding/processing (ver.2.0.0). Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, QC, Canada.