[EN] Traditionally, it has been assumed that the compression of the sign of wavelet coefficients is not worth the effort because they form a zero-mean process. However, several image encoders such as JPEG 2000 include sign-coding capabilities. In this paper, we analyze the convenience of including sign-coding techniques into wavelet-based image encoders and propose a methodology that allows the design of sign-prediction tools for whatever kind of wavelet-based encoder. The proposed methodology is based on the use of metaheuristic algorithms to find the best sign prediction with the most appropriate context distribution that maximizes the resulting sign-compression rate of a particular wavelet encoder. Following our proposal, we have designed and implemented a sign-coding module for the LTW wavelet encoder, to evaluate the benefits of the sign-coding tool provided by our proposed methodology. The experimental results show that sign compression can save up to 18.91% of bit-rate when enabling sign-coding capabilities. Also, we have observed two general behaviors when coding the sign of wavelet coefficients: (a) the best results are provided from moderate to high compression rates; and (b) the sign redundancy may be better exploited when working with high-textured images.This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant RTI2018-098156-B-C54, co-financed by FEDER funds (MINECO/FEDER/UE).López-Granado, OM.; Martínez-Rach, MO.; Martí-Campoy, A.; Cruz-Chávez, MA.; Pérez Malumbres, M. (2020). A General Model for the Design of Efficient Sign-Coding Tools for Wavelet-Based Encoders. Electronics. 9(11):1-17. https://doi.org/10.3390/electronics9111899S117911Said, A., & Pearlman, W. A. (1996). A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, 6(3), 243-250. doi:10.1109/76.499834ISO/IEC 15444-1:2019. Information technology—JPEG 2000 Image Coding System—Part 1: Core Coding Systemhttps://www.iso.org/standard/78321.htmlTaubman, D. (2000). High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, 9(7), 1158-1170. doi:10.1109/83.847830Bilgin, A., Sementilli, P. J., & Marcellin, M. W. (1999). Progressive image coding using trellis coded quantization. IEEE Transactions on Image Processing, 8(11), 1638-1643. doi:10.1109/83.799891Oliver, J., & Malumbres, M. P. (2006). Low-Complexity Multiresolution Image Compression Using Wavelet Lower Trees. IEEE Transactions on Circuits and Systems for Video Technology, 16(11), 1437-1444. doi:10.1109/tcsvt.2006.883505Cho, Y., & Pearlman, W. A. (2007). Hierarchical Dynamic Range Coding of Wavelet Subbands for Fast and Efficient Image Decompression. IEEE Transactions on Image Processing, 16(8), 2005-2015. doi:10.1109/tip.2007.901247Deever, A. T., & Hemami, S. S. (2003). Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs. IEEE Transactions on Image Processing, 12(4), 420-430. doi:10.1109/tip.2003.811499Mallat, S., & Zhong, S. (1992). Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(7), 710-732. doi:10.1109/34.142909López-Granado, O., Galiano, V., Martí, A., Migallón, H., Martínez-Rach, M., Piñol, P., & Malumbres, M. P. (2013). Improving image compression through the use of evolutionary computing algorithms. Data Management and Security. doi:10.2495/data130041Kodak Lossless True Color Image Suitehttp://r0k.us/graphics/kodak/Rawzor—Lossless Compression Software for Camera Raw Imageshttp://imagecompression.info/test_images
