Image quality (IQ) guided multispectral image compression

Abstract

Image compression is necessary for data transportation, which reduces both transfer time and storage space. In this paper, lossy compression is the focus while maintaining a specified image quality (IQ). There are many standard image formats and corresponding compression algorithms, for examples, JPEG (DCT -discrete cosine transform), JPEG 2000 (DWT -discrete wavelet transform), BPG (better portable graphics) and TIFF (LZW -Lempel-Ziv-Welch). The image quality (IQ) of decompressed image will be measured by numerical metrics such as root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural Similarity (SSIM) Index. Given an image and a specified IQ, we demonstrate how to select a compression method and its parameters to achieve an expected IQ. The proposed system consists of 3 steps. The first step is to compress a set of selected images by varying parameters and computing their IQs for each compression method. The second step is to create several regression models for each compression method after analyzing the IQ-measurement versus compression-parameter from a number of compressed images. The third step is to compress the given image with the specified IQ using the selected compression method (JPEG, JPEG2000, BPG, or TIFF) according to the regressed models. The IQ may be specified by a compression ratio (e.g., 50 percent), from which the compression method of the highest IQ (SSIM, or PSNR) is selected. If the IQ is specified by an IQ metric (e.g., SSIM = 0.8, or PSNR = 50), then the compression method of the highest compression ratio will be utilized. The IQguided multispectral compression is demonstrated on thermal (long-wave infrared) images (in gray scales), which shows promising results of a comprehensive method to select compression methods by the IQ.

Multi-focus and multi-modal fusion: a study of multi-resolution transforms

“…Thus, there is a great demand to determine an automatic objective evaluation of quality that aligns itself with the human perception measurement [36]. This would allow for implementation into automatic systems and the use of machine learning based on real-time quality assessment of the output for such applications as image compression [37]. A standardized set of metrics [38,39] for design, analysis, and transition of image fusion algorithms would make it possible to focus and refine the field for more innovative development such as layered sensing [40], situation assessment [41], and usability [42].…”

Prediction of compression-induced image interpretability degradation

“…20 One way to determine the balance between user needs and the compression level desired is through the performance analysis of image quality. [21][22][23] Through the use of image quality, various image processing methods have been developed for cloud architectures. 24,25 An open research question is the alignment of machine-level image interpretability with that of human observers, 26,27 although initial comparisons suggest the human perception and machine-level processing are sensitive to different image characteristics.…”