Modified Quantization Strategy for Enhanced Lossless RAW Audio Compression

Tonny Hidayat; Hendra Kurniawan; Hastari Utama; Dewi Anisa Istiqomah; Joko Dwi Santoso

doi:10.29207/resti.v10i5.7512

Authors

Tonny Hidayat Universitas Amikom Yogyakarta https://orcid.org/0000-0003-3406-0880
Hendra Kurniawan Universitas Amikom Yogyakarta
Hastari Utama Universitas Amikom Yogyakarta
Dewi Anisa Istiqomah Universitas Amikom Yogyakarta
Joko Dwi Santoso Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.29207/resti.v10i5.7512

Keywords:

binary, compression, huffman, lossless

Abstract

The amount of audio data required in today's world is rapidly expanding, posing a significant problem and challenge in terms of media storage and transfer. RAW audio data compression has grown in popularity due to its ability to lower file size without sacrificing quality. To anticipate the complexity of increasing the lossless audio compression ratio, this study proposes a lossless audio data compression algorithm that theoretically increases its efficiency ratio, thus providing multiple compression that combines linear prediction methods, integer wavelet transforms, and as a back-end process using Huffman code for binary output coefficients. The combined transformation produced employs a new prediction form and coefficient process, which is a novelty and the primary contribution of the coding of this algorithm structure. Based on the test results in many audio categories, the obtained results demonstrate that the proposed algorithm surpasses the performance of previous studies. The compression ratio increased from 2% to 5%. The algorithm discovered in this work is ideal for compressing audio with complex patterns and high irregularity using a quick compression procedure.

Downloads

Download data is not yet available.

References

[1] T. Hidayat, M. H. Zakaria, and A. N. C. Pee, “Increasing the Huffman generation code algorithm to equalize compression ratio and time in lossless 16-bit data archiving,” Multimed. Tools Appl., vol. 82, no. 16, pp. 24031–24068, Jul. 2023, doi: 10.1007/s11042-022-14130-1.

[2] S. Nie, L. Gu, Y. Zheng, A. Lam, N. Ono, and I. Sato, “Deeply Learned Filter Response Functions for Hyperspectral Reconstruction,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4767–4776, 2018, doi: 10.1109/CVPR.2018.00501.

[3] C. Zhang, A. Marzougui, and S. Sankaran, “High-resolution satellite imagery applications in crop phenotyping: An overview,” Comput. Electron. Agric., vol. 175, p. 105584, Aug. 2020, doi: 10.1016/j.compag.2020.105584.

[4] Y. Akbari, S. Al-maadeed, O. Elharrouss, F. Khelifi, A. Lawgaly, and A. Bouridane, “Digital forensic analysis for source video identification: A survey,” Forensic Science International: Digital Investigation, vol. 41, p. 301390, Jun. 2022, doi: 10.1016/j.fsidi.2022.301390.

[5] C. E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, Jul. 1948, doi: 10.1002/j.1538-7305.1948.tb01338.x.

[6] D. A. Huffman, “Minimum-Redundancy Codes,” in Proceedings of The I.R.E.,, pp. 1098–1101, Sep. 1952, doi: 10.1109/JRPROC.1952.273898.

[7] T. Hidayat, M. H. Zakaria, and A. N. C. Pee, “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, Nov. 2020, pp. 170–175. doi: 10.1109/ICOIACT50329.2020.9332044.

[8] D. Wang and Y. Zhang, “A lossless compression of remote sensing images based on ANS entropy coding algorithm,” in MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, SPIE, Mar. 2024, p. 21. doi: 10.1117/12.2690099.

[9] J. Zhang and D. Sun, “Improvement of data compression technology for power dispatching based on run length encoding,” Procedia Comput. Sci., vol. 183, pp. 526–532, 2021, doi: 10.1016/j.procs.2021.02.093.

[10] M. Ignatoski, J. Lerga, L. Stanković, and M. Daković, “Comparison of Entropy and Dictionary Based Text Compression in English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian,” Mathematics, vol. 8, no. 7, p. 1059, Jul. 2020, doi: 10.3390/math8071059.

[11] S. Congero and K. Zeger, “Competitive Advantage of Huffman and Shannon-Fano Codes,” IEEE Trans. Inf. Theory, pp. 1–1, 2024, doi: 10.1109/TIT.2024.3417010.

[12] R. Pal, “Speech Compression with Wavelet Transform and Huffman Coding,” in 2021 International Conference on Communication information and Computing Technology (ICCICT), IEEE, Jun. 2021, pp. 1–4. doi: 10.1109/ICCICT50803.2021.9510116.

[13] H. U. Amin, M. Z. Yusoff, and R. F. Ahmad, “A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques,” Biomed. Signal Process. Control, vol. 56, p. 101707, Feb. 2020, doi: 10.1016/j.bspc.2019.101707.

[14] Z. Cheng, Q. Liu, and K. Yang, “A Joint DNA Encoding Approach Based on LZW and Arithmetic Encoding,” IEEE Trans. Mol. Biol. Multiscale Commun., vol. 11, no. 2, pp. 237–245, Jun. 2025, doi: 10.1109/TMBMC.2025.3556858.

[15] R. Leelavathi and M. N. Giri Prasad, “High-Capacity Reversible Data Hiding Using Lossless LZW Compression,” 2022, pp. 517–528. doi: 10.1007/978-3-030-86165-0_44.

[16] R. Leelavathi and M. N. Giri Prasad, “High-Capacity Reversible Data Hiding Using Lossless LZW Compression,” 2022, pp. 517–528. doi: 10.1007/978-3-030-86165-0_44.

[17] X. Dong and P. Li, “Implementation of A Real-Time Lossless JPEG-LS Compression Algorithm Based on FPGA,” in 2022 14th International Conference on Signal Processing Systems (ICSPS), IEEE, Nov. 2022, pp. 523–528. doi: 10.1109/ICSPS58776.2022.00096.

[18] M. J. Weinberger, G. Seroussi, and G. Sapiro, “The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS,” IEEE Transactions on Image Processing, vol. 9, no. 8, pp. 1309–1324, 2000, doi: 10.1109/83.855427.

[19] S. Jeyalaksshmi, D. Akila, D. Padmapriya, G. Suseendran, and S. Pal, “Human Facial Expression Based Video Retrieval with Query Video Using EBCOT and MLP,” 2021, pp. 157–166. doi: 10.1007/978-981-33-4389-4_16.

[20] T. Lyngby, R. R. Nylandsted, R. Glück, and T. Yokoyama, “Towards Clean Reversible Lossless Compression,” 2024, pp. 94–102. doi: 10.1007/978-3-031-62076-8_7.

[21] H. Rhee, Y. Il Jang, S. Kim, and N. I. Cho, “Channel-Wise Progressive Learning For Lossless Image Compression,” in 2020 IEEE International Conference on Image Processing (ICIP), IEEE, Oct. 2020, pp. 1113–1117. doi: 10.1109/ICIP40778.2020.9191322.

[22] Y. Ding et al., “Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity,” Front. Genet., vol. 14, Jun. 2023, doi: 10.3389/fgene.2023.1213907.

[23] O. Giudice, D. Allegra, F. Stanco, G. Grasso, and S. Battiato, “A Fast Palette Reordering Technique Based on GPU-Optimized Genetic Algorithms,” in 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, Oct. 2018, pp. 1138–1142. doi: 10.1109/ICIP.2018.8451221.

[24] P. Singhai, A. Kumar, A. Ateek, I. A. Ansari, G. K. Singh, and H. N. Lee, “ECG Signal Compression Based on Optimization of Wavelet Parameters and Threshold Levels Using Evolutionary Techniques,” Circuits Syst. Signal Process., vol. 42, no. 6, pp. 3509–3537, Jun. 2023, doi: 10.1007/s00034-022-02280-4.

[25] E. C. Kaya and I. Tabus, “Lossless Compression of Point Cloud Sequences Using Sequence Optimized CNN Models,” IEEE Access, vol. 10, pp. 83678–83691, 2022, doi: 10.1109/ACCESS.2022.3197295.

[26] Y. Liu, “Recovery of Lossy Compressed Music Based on CNN Super-Resolution and GAN,” in 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC), IEEE, Nov. 2021, pp. 623–629. doi: 10.1109/ICFTIC54370.2021.9647041.

[27] B. M. Umapathi, K. Chauhan, P. Shenoy, and D. Sridharan, “Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, California: International Joint Conferences on Artificial Intelligence Organization, Aug. 2023, pp. 1440–1450. doi: 10.24963/ijcai.2023/160..

[28] F. Mentzer and M. Tschannen, “Learning better lossless compression using lossy compression,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6638--6647, 2020, doi: 10.3929/ethz-b-000476480.

[29] M. Li et al., “Understanding is Compression,” Sep. 06, 2024. doi: 10.21203/rs.3.rs-4735292/v1.

[30] F. Sahinuc and A. Koc, “Fractional Fourier Transform Meets Transformer Encoder,” IEEE Signal Process. Lett., vol. 29, pp. 2258–2262, 2022, doi: 10.1109/LSP.2022.3217975.

[31] N. A. N. Azman, S. Ali, R. A. Rashid, F. Asraf Saparudin, and M. Adib Sarijari, “A hybrid predictive technique for lossless image compression,” Bulletin of Electrical Engineering and Informatics, vol. 8, no. 4, Dec. 2019, doi: 10.11591/eei.v8i4.1612.

[32] K. Bannajak, N. Theera-Umpon, and S. Auephanwiriyakul, “Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction,” Int. J. Environ. Res. Public Health, vol. 20, no. 3, p. 2753, Feb. 2023, doi: 10.3390/ijerph20032753.

[33] S. Pramanik, “An adaptive image steganography approach depending on integer wavelet transform and genetic algorithm,” Multimed. Tools Appl., vol. 82, no. 22, pp. 34287–34319, Sep. 2023, doi: 10.1007/s11042-023-14505-y.

[34] R. Pinto and K. Shama, “An Efficient Architecture for Modified Lifting-Based Discrete Wavelet Transform,” Sens. Imaging, vol. 21, no. 1, p. 53, Dec. 2020, doi: 10.1007/s11220-020-00317-z.

[35] R. Pinto and J. C. Saldanha, “Dual-scan architecture for 2D discrete wavelet transform using modified lifting algorithm,” International Journal of Systems, Control and Communications, vol. 12, no. 4, p. 327, 2021, doi: 10.1504/IJSCC.2021.118587.

[36] T. D. Tran, “Modern transform design for practical audio/image/video coding applications,” in Signal Processing and Machine Learning Theory, Elsevier, 2024, pp. 495–536. doi: 10.1016/B978-0-32-391772-8.00014-4.

[37] W. Hassen and H. Amiri, “The 5/3 and 9/7 wavelet filters study in a sub-bands image coding,” in 2013 7th IEEE International Conference on e-Learning in Industrial Electronics (ICELIE), IEEE, Nov. 2013, pp. 150–154. doi: 10.1109/ICELIE.2013.6701290.

[38] M. Sushmitha, S. Chetan, and S. Sarkar, “An Efficient High-Speed Lifting Based 1D/2D-DWT VLSI Architecture Using CDF-5/3 Wavelet Transform For Image Processing Applications,” in 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), IEEE, Nov. 2020, pp. 269–274. doi: 10.1109/RTEICT49044.2020.9315649.

[39] S. T. Klein, S. Saadia, and D. Shapira, “Forward Looking Huffman Coding,” Theory Comput. Syst., vol. 65, no. 3, pp. 593–612, Apr. 2021, doi: 10.1007/s00224-020-09992-7.