Abstract
Owing to the development of multimedia technology, it is mandatory to perform image compression, while transferring an image from one end to another. The proposed method directly highlights the salient region in WHT domain, which results in the saliency map with lesser computation. The WHT-based saliency map is directly used to guide the image compression. Initially, the important and less important regions are identified using WHT-based visual saliency model. It significantly reduces the entropy and also reserves perceptual fidelity. The main aim of the proposed method is to produce the high-quality compressed images with lesser computational effort and thereby achieving high compression ratio. Due to the simplicity and high speed of WHT, the proposed visual saliency-based image compression method is producing reliable results, in terms of peak signal-to-noise ratio (PSNR), compression ratio, and structural similarity (SSIM), compared to the state-of-the-art methods.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guo C, Zhang L (2010) A novel multi resolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans Image Process 19, 185–198
Arya R, Singh N, Agrawal RK (2015) A novel hybrid approach for salient object detection using local and global saliency in frequency domain. Multimed Tools Appl. doi:10.1007/s11042-015-2750-y
Imamoglu N, Lin WS, Fang YM (2013) A saliency detection model using low-level features based on wavelet transform. IEEE Trans Multimedia 15:96–105
Lin RJ, Lin WS (2014) Computational visual saliency model based on statistics and machine learning. J Vision 14(9), 1–18
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259
Ma YF, Zhang HJ (2003) Contrast-based image attention analysis by using fuzzy growing. In: ACM International conference on multimedia, pp 374–381, Berkeley (2003)
Harel J, Koch C, Perona P (2006) Graph-based visual saliency. Neural Inf Process Syst 545–552
Goferman S, Zelnik Manor L, Tal A Context-aware saliency detection. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2376–2383
Achanta R, Hemami S, Estrada F, Susstrunk (2009) Frequency-tuned salient region detection. IEEE conference on computer vision and pattern recognition, pp 1597–1604
Cheng MM, Zhang GX, Mitra NJ, Huang X, Hu SM (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582
Perazzi F, Krahenbuhl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. In IEEE conference on computer vision and pattern recognition (CVPR), pp 733–740
Guo C, Ma Q, Zhang L (2008) Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1–8
Li J, Levine MD, An X, Xu X, He H (2013) Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans Pattern Anal Mach Intell 35:996–1010
Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 1–8
Merry RJE (2005) Wavelet theory and application-a literature study. Eindhoven University of Technology, The Netherlands
Li ZQ, Fang T, Huo H (2010) A saliency model based on wavelet transform and visual attention. Sci China Inf Sci 53(4):738–751
Tian Q, Sebe N, Lew MS, Loupias E, Huang TS (2001) Image retrieval using wavelet-based salient points. Electron Imag 10(4):835–849
Candes E, Donoho D (2004) New tight frames of curvelets and optimal representations of objects with piecewise singularities. Commun Pure Appl Math 57:219–266
Bao L, Lu J, Li Y, Shi Y (2014) A saliency detection model using shearlet transform. Multimed Tools Appl. doi:10.1007/s11042-014-2043-x(2014)
Yu Y, Yang J (2016) Visual saliency using binary spectrum of Walsh–Hadamard transform and its applications to ship detection in multispectral imagery. Neural Process Lett. doi:10.1007/s11063-016-9507-0
Guo C, Zhang L (2010) A novel multi resolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans Image Process 19(1):185–198
Hadizadeh H, Bajic I (2014) Saliency-aware video compression. IEEE Trans Image Process 23(1):19–33
Barua S, Mitra K, Veeraraghavan A (2015) Saliency guided Wavelet compression for low-bitrate Image and Video coding. In: IEEE global conference on signal and information processing (2015)
Ouerhani N, Bracamonte J, Hugli H, Ansorge M, Pellandini F (2001) Adaptive color image compression based on visual attention. International conference on image analysis and processing, pp 26–28
Li Z, Qin S, Itti L (2011) Visual attention guided bit allocation in video compression. Image Vision Comp 29(1):1–14
Hadizadeh H (2013) visual saliency in video compression and transmission. Thesis dissertation (2013)
Ho-Phuoc T, Dupret A, Alacoque L (2012) Saliency-based data compression for image sensors. Sensors, IEEE
Zundy F, Pritch Y, Sorkine-Hornung A, Mangold S, Gross T (2013) Content-aware compression using saliency-driven image retargeting. In: 20th IEEE international conference on image processing (ICIP) (2013)
Tu Q, Mena A, Jiang Z, Ye F, Xu J (2015) Video saliency detection incorporating temporal information in compressed domain. Signal Process: Image Commun 38, 32–44
Yu SX, Lisin DA (2009) Image compression based on visual saliency at individual scales. In International symposium on visual computing, USA
Dhavale N, Itti L (2003) Saliency-based multi-foveated MPEG compression. In: Proceedings of signal processing and its applications pp 229–232 (2003)
Duan L, Ke C (2012) A natural image compression approach based on independent component analysis and visual saliency detection. Adv Sci Lett 5, 1–4
Lakshmi Priya GG, Domnic S (2014) Walsh-Hadamard transform kernel-based feature vector for shot boundary detection. IEEE Trans Image Process 23(12):5187–5197
Petrov Y, Li Z (2003) Local correlations, information redundancy, and sufficient pixel depth in natural images. J Opt Soc Am A 20(1):56–66
Li Z, Atick JJ (1994) Toward a theory of the striate cortex. Neural Comput 6(1):127–146
Luo W, Li H, Liu G, Ngan KN (2012) Global salient information maximization for saliency detection. Sig Process: Image Commun 27(3):238–248
Ma X, Xie X, Lam K-M, Zhong Y (2015) Efficient saliency analysis based on wavelet transform and entropy theory. J Vis Commun Image R 30:201–207
Acharya T, Tsai PS (2005) JPEG2000 standard for image compression: concepts, algorithms and VLSI architecture. Wiley 60(2005)
Gupta R, Khanna MT, Chaudhur S (2013) Visual saliency guided video compression algorithm. Signal Process: Image Commun 28:1006–1022
Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. In: IEEE conference on computer vision and pattern recognition (CVPR)
Murray N, Vanrell M, Otazu X, Parraga CA (2011) Saliency estimation using a non-parametric low-level vision model. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 433–440
Gonzalez RC, Woods RE, Eddins SL (2004) Digital signal procesing using Matlab. Prentice Hall, Englewood Cliffs, NJ
Wallace GK (1991) The JPEG still picture compression standard. Commun ACM 34:31–44
Chowdhury MMH, Khatun A (2012) Image compression using discrete wavelet transform. Int J Comput Sci 9(4)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Diana Andrushia, A., Thangarjan, R. (2018). Saliency-Based Image Compression Using Walsh–Hadamard Transform (WHT). In: Hemanth, J., Balas , V. (eds) Biologically Rationalized Computing Techniques For Image Processing Applications. Lecture Notes in Computational Vision and Biomechanics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-61316-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-61316-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61315-4
Online ISBN: 978-3-319-61316-1
eBook Packages: EngineeringEngineering (R0)