Perceptually Lossless Compression for Mastcam Multispectral Images: A Comparative Study

Abstract

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.

Reconstruction of Compressed Hyperspectral Image Using SqueezeNet Coupled Dense Attentional Net

“…For compressing [33,34] the input HSI, this model introduces the predicted-based TSF method. In this method, an extension of the prediction-based recursive least square (RLS) filter [35] is employed over the denoised image that are obtained from the SqueezeNet model.…”

A Brief Review of Some Interesting Mars Rover Image Enhancement Projects

“…The objective of our recent study [12] was to perform thorough comparative studies and advocated the importance of using perceptually lossless compression for NASA's missions. In particular, in our recent paper [12], we evaluated five image codecs, including Daala, X265, X264, J2k, and JPEG.…”

“…The objective of our recent study [12] was to perform thorough comparative studies and advocated the importance of using perceptually lossless compression for NASA's missions. In particular, in our recent paper [12], we evaluated five image codecs, including Daala, X265, X264, J2k, and JPEG. The objective is to investigate which one of the above codecs can attain a 10:1 compression ratio, which we consider as perceptually lossless compression.…”

Learned Hyperspectral Compression Using a Student’s T Hyperprior

“…For example, JP3D (part 10 in JPEG2000) [13] is designed for 3D image compression, yet does not work well for HSIs due to the fact that the 3D transform in JP3D is isotropic, but the spectral correlation in HSIs is much higher than the spatial direction [14]. Thus, since HSIs can be viewed as an anisotropic joint of 1D spectra and 2D space, a 1D transform (such as the Karhunen-Loève Transform (KLT), DCT or DWT) in the spectral dimension combined with a 2D transform in space has become a popular and effective solution [15][16][17][18].…”