research-article

Wavelet-Based Spectral–Spatial Transforms for CFA-Sampled Raw Camera Image Compression

Author:

Taizo SuzukiAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 29

Pages 433 - 444

https://doi.org/10.1109/TIP.2019.2928124

Published: 01 January 2020 Publication History

Abstract

Spectral–spatial transforms (SSTs) change a raw camera image captured using a color filter array (CFA-sampled image) from an RGB color space composed of red, green, and blue components into a decorrelated color space, such as YDgCbCr or YDgCoCg color space composed of luma, difference green, and two chroma components. This paper describes three types of wavelet-based SST (WSST) obtained by reorganizing all of the existing SSTs covered in this paper. First, we introduce three types of macropixel SST (MSST) implemented within each <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> macropixel. Next, we focus on two-channel Haar wavelet transforms, which are simple wavelet transforms, and three-channel Haar-like wavelet transforms in each MSST and replace the Haar and Haar-like wavelet transforms with Cohen–Daubechies–Feauveau (CDF) 5/3 and 9/7 wavelet transforms, which are customized on the basis of the original pixel positions in 2D space. Although the test data set is not big, in lossless CFA-sampled image compression based on JPEG 2000, the WSSTs improve the bitrates by about 1.67%–3.17% compared with not using a transform, and the WSSTs that use 5/3 wavelet transforms improve the bitrates by about 0.31%–0.71% compared with the best existing SST. Moreover, in lossy CFA-sampled image compression based on JPEG 2000, the WSSTs show about 2.25–4.40 dB and 26.04%–49.35% in the Bjøntegaard metrics (BD-PSNRs and BD-rates) compared with not using a transform, and the WSSTs that use 9/7 wavelet transforms improve the metrics by about 0.13–0.40 dB and 2.27%–4.80% compared with the best existing SST.

References

[1]

G. K. Wallace, “The JPEG still picture compression standard,” IEEE Trans. Consum. Electron., vol. 38, no. 1, pp. 18–34, Feb. 1992.

Digital Library

[2]

A. Skodras, C. Christopoulis, and T. Ebrahimi, “The JPEG2000 still image compression standard,” IEEE Signal Process. Mag., vol. 18, no. 5, pp. 36–58, Sep. 2001.

[3]

J. L. Mitchell, W. B. Pennebaker, C. Fogg, and D. J. LeGall, MPEG Video Compression Standard. Dordrecht, The Netherlands: Kluwer, 2000.

[4]

F. Dufaux, G. J. Sullivan, and T. Ebrahimi, “The JPEG XR image coding standard,” IEEE Signal Process. Mag., vol. 26, no. 6, pp. 195–199, Nov. 2009.

[5]

T. Wiegand, G. J. Sullivan, G. Bjøntegaard, and A. Luthra, “Overview of the H.264/AVC video coding standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 560–576, Jul. 2003.

Digital Library

[6]

G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, “Overview of the high efficiency video coding (HEVC) standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1649–1668, Dec. 2012.

Digital Library

[7]

H. S. Malvar, G. J. Sullivan, and S. Srinivasan, “Lifting-based reversible color transformations for image compression,” Proc. SPIE, vol. 7073, Aug. 2008, Art. no.

[8]

S. C. Pei and J. J. Ding, “Reversible integer color transform,” IEEE Trans. Image Process., vol. 16, no. 6, pp. 1686–1691, Jun. 2007.

Digital Library

[9]

T. Strutz, “Multiplierless reversible color transforms and their automatic selection for image data compression,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 7, pp. 1249–1259, Jul. 2013.

Digital Library

[10]

R. Starosolski, “New simple and efficient color space transformations for lossless image compression,” J. Vis. Commun. Image Represent., vol. 25, no. 5, pp. 1056–1063, Jul. 2014.

[11]

T. Strutz and A. Leipnitz, “Reversible color spaces without increased bit depth and their adaptive selection,” IEEE Signal Process. Lett., vol. 22, no. 9, pp. 1269–1273, Sep. 2015.

[12]

Kodak Lossless True Color Image Suite. Accessed: Jul. 26, 2019. [Online]. Available: http://r0k.us/graphics/kodak/

[13]

C. C. Koh, J. Mukherjee, and S. K. Mitra, “New efficient methods of image compression in digital cameras with color filter array,” IEEE Trans. Consum. Electron., vol. 49, no. 4, pp. 1448–1456, Nov. 2003.

Digital Library

[14]

K.-H. Chung and Y.-H. Chan, “A lossless compression scheme for Bayer color filter array images,” IEEE Trans. Image Process., vol. 17, no. 2, pp. 134–144, Feb. 2008.

Digital Library

[15]

S. Kim and N. I. Cho, “Lossless compression of color filter array images by hierarchical prediction and context modeling,” IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 6, pp. 1040–1046, Jun. 2014.

[16]

M. Lakshmi, J. Senthilkumar, and Y. Suresh, “Visually lossless compression for Bayer color filter array using optimized vector quantization,” Appl. Soft Comput., vol. 46, pp. 1030–1042, Sep. 2016.

Digital Library

[17]

N. Zhang and X. Wu, “Lossless compression of color mosaic images,” IEEE Trans. Image Process., vol. 15, no. 6, pp. 1379–1388, Jun. 2006.

Digital Library

[18]

H. S. Malvar and G. J. Sullivan, “Progressive-to-lossless compression of color-filter-array images using macropixel spectral–spatial transformation,” in Proc. DCC, Snowbird, UT, USA, Apr. 2012, pp. 3–12.

[19]

M. Hernández-Cabronero, M. W. Marcellin, I. Blanes, and J. Serra-Sagristà, “Lossless compression of color filter array mosaic images with visualization via JPEG 2000,” IEEE Trans. Multimedia, vol. 20, no. 2, pp. 257–270, Feb. 2018.

Digital Library

[20]

Y. Lee, K. Hirakawa, and T. Q. Nguyen, “Camera-aware multi-resolution analysis for raw image sensor data compression,” IEEE Trans. Image Process., vol. 27, no. 6, pp. 2806–2817, Jun. 2018.

[21]

A. Cohen, I. Daubechies, and J.-C. Feauveau, “Biorthogonal bases of compactly supported wavelets,” Commun. Pure Appl. Math., vol. 45, no. 5, pp. 485–560, Jun. 1992.

[22]

T. Suzuki, “Lossless compression of CFA-sampled images using YDgCoCg transforms with CDF wavelets,” in Proc. ICIP, Athens, Greece, Oct. 2018, pp. 1143–1147.

[23]

I. Daubechies and W. Sweldens, “Factoring wavelet transforms into lifting steps,” J. Fourier Anal. Appl., vol. 4, no. 3, pp. 247–269, May 1998.

[24]

V. Bychkovsky, S. Paris, E. Chan, and F. Durand, “Learning photographic global tonal adjustment with a database of input/output image pairs,” in Proc. CVPR, Colorado Springs, CO, USA, Jun. 2011, pp. 97–104.

[25]

S. Andriani, H. Brendel, T. Seybold, and J. Goldstone, “Beyond the Kodak image set: A new reference set of color image sequences,” in Proc. ICIP, Melbourne, VIC, Australia, Sep. 2013, pp. 2289–2293.

[26]

M. J. T. Smith and S. L. Eddins, “Analysis/synthesis techniques for subband image coding,” IEEE Trans. Acoust., Speech, Signal Process., vol. 38, no. 8, pp. 1446–1456, Aug. 1990.

[27]

M. Iwahashi and H. Kiya, “Non separable 2D factorization of separable 2D DWT for lossless image coding,” in Proc. ICIP, Cairo, Egypt, Nov. 2009, pp. 17–20.

[28]

T. Strutz and I. Rennert, “Two-dimensional integer wavelet transform with reduced influence of rounding operations,” EURASIP J. Adv. Signal Process., vol. 2012, no. 75, pp. 1–18, Apr. 2012.

Cited By

Zhang JJia SYu ZHuang TWilliams BChen YNeville J(2023)Learning temporal-ordered representation for spike streams based on discrete wavelet transformsProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i1.25085(137-147)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i1.25085
Mohebbian MChafjiri FVedaei SWahid K(2023)CFA image compression using an efficient cascaded overlapping color transformationMultimedia Tools and Applications10.1007/s11042-023-15352-782:28(43233-43250)Online publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-023-15352-7
Chen DLi YChen JBi HDing X(2022)Differential Privacy via Haar Wavelet Transform and Gaussian Mechanism for Range QueryComputational Intelligence and Neuroscience10.1155/2022/81398132022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8139813
Show More Cited By

Index Terms

Wavelet-Based Spectral–Spatial Transforms for CFA-Sampled Raw Camera Image Compression
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image compression
    2. Image manipulation

Index terms have been assigned to the content through auto-classification.

Recommendations

Lossless image compression with projection-based and adaptive reversible integer wavelet transforms

Reversible integer wavelet transforms are increasingly popular in lossless image compression, as evidenced by their use in the recently developed JPEG2000 image coding standard. In this paper, a projection-based technique is presented for decreasing the ...
Lossless compression of CFA sampled image using decorrelated Mallat wavelet packet decomposition
2017 IEEE International Conference on Image Processing (ICIP)
This paper presents a rigorous analysis of wavelet transform on color filter array (CFA) sampled images. The presented analysis suggests that the wavelet coefficients of HL and LH subbands are highly correlated. Hence, we propose a novel lossless ...
Context-based embedded image compression using binary wavelet transform

Binary wavelet transform (BWT) has several distinct advantages over the real wavelet transform (RWT), such as the conservation of alphabet size of wavelet coefficients, no quantization introduced during the transform and the simple Boolean operations ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 29, Issue

2020

3918 pages

ISSN:1057-7149

Issue’s Table of Contents

1057-7149 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

Publisher

IEEE Press

Publication History

Published: 01 January 2020

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang JJia SYu ZHuang TWilliams BChen YNeville J(2023)Learning temporal-ordered representation for spike streams based on discrete wavelet transformsProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i1.25085(137-147)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i1.25085
Mohebbian MChafjiri FVedaei SWahid K(2023)CFA image compression using an efficient cascaded overlapping color transformationMultimedia Tools and Applications10.1007/s11042-023-15352-782:28(43233-43250)Online publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-023-15352-7
Chen DLi YChen JBi HDing X(2022)Differential Privacy via Haar Wavelet Transform and Gaussian Mechanism for Range QueryComputational Intelligence and Neuroscience10.1155/2022/81398132022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8139813
Jianyun LJunming SChunling W(2022)Multi-level clustering based on cluster order constructed with dynamic local densityApplied Intelligence10.1007/s10489-022-03830-853:8(9744-9761)Online publication date: 12-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-03830-8
Richter TFößel SDescampe ARouvroy G(2021)Bayer CFA Pattern Compression With JPEG XSIEEE Transactions on Image Processing10.1109/TIP.2021.309542130(6557-6569)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIP.2021.3095421

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents