Optimized satellite image compression and reconstruction via evolution strategies
نویسندگان
چکیده
This paper describes the automatic discovery, via an Evolution Strategy with Covariance Matrix Adaptation (CMAES), of vectors of real-valued coefficients representing matched forward and inverse transforms that outperform the 9/7 Cohen-Daubechies-Feauveau (CDF) discrete wavelet transform (DWT) for satellite image compression and reconstruction under conditions subject to quantization error. The best transform evolved during this study reduces the mean squared error (MSE) present in reconstructed satellite images by an average of 33.78% (1.79 dB), while maintaining the average information entropy (IE) of compressed images at 99.57% in comparison to the wavelet. In addition, this evolved transform achieves 49.88% (3.00 dB) average MSE reduction when tested on 80 images from the FBI fingerprint test set, and 42.35% (2.39 dB) average MSE reduction when tested on a set of 18 digital photographs, while achieving average IE of 104.36% and 100.08%, respectively. These results indicate that our evolved transform greatly improves the quality of reconstructed images without substantial loss of compression capability over a broad range of image classes.
منابع مشابه
Evolving wavelet and scaling numbers for optimized image compression: forward, inverse, or both? A comparative study
The 9/7 wavelet is used for a wide variety of image compression tasks. Recent research, however, has established a methodology for using evolutionary computation to evolve wavelet and scaling numbers describing transforms that outperform the 9/7 under lossy conditions, such as those brought about by quantization or thresholding. This paper describes an investigation into which of three possible...
متن کاملState-of-the-art lossy compression of Martian images via the CMA-ES evolution strategy
The research described in this paper uses the CMAES evolution strategy to optimize matched forward and inverse transform pairs for the compression and reconstruction of images transmitted from Mars rovers under conditions subject to quantization error. Our best transforms outperform both the integer and floating-point implementations of the 2/6 wavelet, substantially reducing error in reconstru...
متن کاملON A LOSSY IMAGE COMPRESSION/RECONSTRUCTION METHOD BASED ON FUZZY RELATIONAL EQUATIONS
The pioneer work of image compression/reconstruction based onfuzzy relational equations (ICF) and the related works are introduced. TheICF regards an original image as a fuzzy relation by embedding the brightnesslevel into [0,1]. The compression/reconstruction of ICF correspond to thecomposition/solving inverse problem formulated on fuzzy relational equations.Optimizations of ICF can be consequ...
متن کاملAn Optimum Novel Technique Based on Golomb-Rice Coding for Lossless Image Compression of Digital Images
New innovative research trends are more essential in the area of image compression for various imaging applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the com...
متن کاملResearch on the Compression Algorithm of the Infrared Thermal Image Sequence Based on Differential Evolution and Double Exponential Decay Model
This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time ...
متن کامل