The two mast cameras, Mastcams, onboard Mars rover
Curiosity are multispectral imagers with nine bands in each. Currently, the
images are compressed losslessly using JPEG, which can achieve only two to
three times of compression. We present a comparative study of four approaches
to compressing multispectral Mastcam images. The first approach is to divide
the nine bands into three groups with each group having three bands. Since the
multispectral bands have strong correlation, we treat the three groups of
images as video frames. We call this approach the Video approach. The second
approach is to compress each group separately and we call it the split band
(SB) approach. The third one is to apply a two-step approach in which the first
step uses principal component analysis (PCA) to compress a nine-band image cube
to six bands and a second step compresses the six PCA bands using conventional
codecs. The fourth one is to apply PCA only. In addition, we also present
subjective and objective assessment results for compressing RGB images because
RGB images have been used for stereo and disparity map generation. Five
well-known compression codecs, including JPEG, JPEG-2000 (J2K), X264, X265,
and Daala in the literature, have been applied and compared in each approach.
The performance of different algorithms was assessed using four well-known
performance metrics. Two are conventional and another two are known to have
good correlation with human perception. Extensive experiments using actual Mastcam images have been performed to
demonstrate the various approaches. We observed that perceptually lossless
compression can be achieved at 10:1 compression ratio. In particular, the
performance gain of the SB approach with Daala is at least 5 dBs in terms peak
signal-to-noise ratio (PSNR) at 10:1 compression ratio over that of JPEG.
Subjective comparisons also corroborated with the objective metrics in
that perceptually lossless compression can be achieved even at 20 to 1
compression.
References
[1]
Strang, G. and Nguyen, T. (1997) Wavelets and Filter Banks. Wellesley-Cambridge Press, Wellesley, MA.
[2]
Bell, J.F., et al. (2017) The Mars Science Laboratory Curiosity Rover Mast Camera (Mastcam) Instruments: Pre-Flight and in-Flight Calibration, Validation, and Data Archiving. AGU Journal Earth and Space Science, 4, 396-452.
https://doi.org/10.1002/2016EA000219
[3]
Ayhan, B., Kwan, C. and Vance, S. (2015) On the Use of a Linear Spectral Unmixing Technique for Concentration Estimation of APXS Spectrum. Journal of Multidisciplinary Engineering Science and Technology, 2, 2469-2474.
[4]
Wang, W., Li, S., Qi, H., Ayhan, B., Kwan, C. and Vance, S. (2014) Revisiting the Preprocessing Procedures for Elemental Concentration Estimation Based on CHEMCAM LIBS on MARS Rover. 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland, 24-27 June 2014, 1-4. https://doi.org/10.1109/WHISPERS.2014.8077520
[5]
Wang, W., Ayhan, B., Kwan, C., Qi, H. and Vance, S. (2014) A Novel and Effective Multivariate Method for Compositional Analysis Using Laser Induced Breakdown Spectroscopy. IOP Conference Series: Earth and Environmental Science, 17, Article ID: 012208. https://doi.org/10.1088/1755-1315/17/1/012208
[6]
Ayhan, B., Dao, M., Kwan, C., Chen, H., Bell, J.F. and Kidd, R. (2017) A Novel Utilization of Image Registration Techniques to Process Mastcam Images in Mars Rover with Applications to Image Fusion, Pixel Clustering, and Anomaly Detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4553-4564. https://doi.org/10.1109/JSTARS.2017.2716923
[7]
Kwan, C., Dao, M., Chou, B., Kwan, L.M. and Ayhan, B. (2017) Mastcam Image Enhancement Using Estimated Point Spread Functions. 2017 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York City, 19-21 October 2017, 186-191.
https://doi.org/10.1109/UEMCON.2017.8249023
[8]
Kwan, C., Chou, B. and Ayhan B. (2018) Enhancing Stereo Image Formation and Depth Map Estimation for Mastcam Images. 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York City, 8-10 November 2018, 566-572. https://doi.org/10.1109/UEMCON.2018.8796542
[9]
Kwan, C. and Larkin, J. (2018) Perceptually Lossless Compression for Mastcam Images. 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York City, 8-10 November 2018, 559-565.
https://doi.org/10.1109/UEMCON.2018.8796824
Tran, T.D., Liang, J. and Tu, C. (2003) Lapped Transform via Time-Domain Pre- and Post-Filtering. IEEE Transactions on Signal Processing, 51, 1557-1571.
https://doi.org/10.1109/TSP.2003.811222
[15]
Daala. http://xiph.org/daala/
[16]
Kwan, C., Larkin, J., Budavari, B. and Chou, B. (2019) Compression Algorithm Selection for Multispectral Mastcam Images. Signal & Image Processing: An International Journal (SIPIJ), 10, 1-14. https://doi.org/10.5121/sipij.2019.10101
[17]
Kwan, C. and Larkin, J. (2019) New Results in Perceptually Lossless Compression of Hyperspectral Images. Journal of Signal and Information Processing, 10, 96-124.
https://doi.org/10.4236/jsip.2019.103007
[18]
Ayhan, B., Kwan, C. and Zhou, J. (2018) A New Nonlinear Change Detection Approach Based on Band Ratioing. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, No. 1064410.
[19]
Haykin, S. (1993) Neural Networks and Learning Machines. Pearson Education, Boston, MA.
[20]
Wu, J., Liang, Q. and Kwan, C. (2012) A Novel and Comprehensive Compressive Sensing Based System for Data Compression. 2012 IEEE Globecom Workshops, Anaheim, CA, 3-7 December 2012, 1420-1425.
https://doi.org/10.1109/GLOCOMW.2012.6477792
[21]
Zhou, J. and Kwan, C. (2018) A Hybrid Approach for Wind Tunnel Data Compression. 2018 Data Compression Conference, Snowbird, UT, 27-30 March 2018, 435.
https://doi.org/10.1109/DCC.2018.00088
[22]
Kwan, C. and Luk Y. (2018) Hybrid Sensor Network Data Compression with Error Resiliency. 2018 Data Compression Conference, Snowbird, UT, 27-30 March 2018, 416. https://doi.org/10.1109/DCC.2018.00069
[23]
Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J. and Lukin, V. (2007) On between-Coefficient Contrast Masking of DCT Basis Functions. Proceedings of the Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics VPQM-07, Scottsdale, AZ, 13-15 January 2010.
[24]
JPEG-XR. http://en.wikipedia.org/wiki/JPEG_XR
[25]
VP8. http://en.wikipedia.org/wiki/VP8
[26]
VP9. http://en.wikipedia.org/wiki/VP9
[27]
Kwan, C., Li, B., Xu, R., Tran, T. and Nguyen, T. (2001) Very Low-Bit-Rate Video Compression Using Wavelets. Wavelet Applications VIII, 4391, 76-180.
https://doi.org/10.1117/12.421197
[28]
Kwan, C., Li, B., Xu, R., Tran, T. and Nguyen, T. (2001) SAR Image Compression Using Wavelets. Wavelet Applications VIII, 4391, 349-357.
https://doi.org/10.1117/12.421215
[29]
Kwan, C., Li, B., Xu, R., Li, X., Tran, T. and Nguyen, T.Q. (2006) A Complete Image Compression Codec Based on Overlapped Block Transform. EUROSIP Journal of Applied Signal Processing, 2006, Article No. 010968.
https://doi.org/10.1155/ASP/2006/10968
[30]
Kwan, C., Shang, E. and Tran, T. (2018) Perceptually Lossless Image Compression with Error Recovery. 2nd International Conference on Vision, Image and Signal Processing, Las Vegas, NV, 27-29 August 2018, Article No. 16.
https://doi.org/10.1145/3271553.3271602
[31]
Kwan, C., Shang, E. and Tran, T. (2018) Perceptually Lossless Video Compression with Error Concealment. 2nd International Conference on Vision, Image and Signal Processing, Las Vegas, NV, 27-29 August 2018, Article No. 19.
https://doi.org/10.1145/3271553.3271622
