Fractal image compression (FIC) is an auspicious technique for encoding images. It is based on fractals that can be found in an image and the generation of duplicating blocks based on mathematical transformations. The method appears exciting in both theory and application but has a disadvantage in encoding time due to the great resources needed when encoding big data. On another side, heuristics represent a set of approaches used to resolve hard optimization tasks with lucid resources consumption. They are branded with their fast convergence and reduction of research difficulty. In this paper, we apply and for the first time, a new study about the performance of the Bat Inspired Algorithm (BIA) as a natural inspired metaheuristic to improve FIC. Results show the improvement of such algorithm under different aspects (encoding time, compression ratio (CR), peak signal to noise ratio (PSNR), and mean square error (MSE)). Furthermore, a comparison with some of the current methods underlines this gain.

Fractal encoding is a promising method of image compression. It is built on the basis of the forms found in the image and the generation of repetitive blocks based on mathematical translations. The technique seems to be moved theoretically and practically, but it requires enormous programming time due to the excessive resources required when compressing large volumes of data. On the other hand, metaheuristics represent all of the methods used to solve difficult optimization problems with less consumption of resources. They are marked by their rapid convergence and their lessening in research difficulties. In this chapter, we have tried to apply a new experience around the performance of organic metaheuristics inspired by nature, which are, respectively, the wolf pack algorithm (WPA) and the bat-inspired algorithm (BIA), as bioinspired techniques to optimize the fractal image compression (FIC). Experiments show the enhancement of diverse characteristics (coding time, compression rate (CR), peak signal-to-noise ratio (PSNR), and mean square error (MSE)). In addition, an assessment of the proposed approaches via many other approaches highlights this improvement.

Several studies by psychologists and computer scientists have verified the link between handwriting and writer gender. The texture of the writing image is a major indicator of whether it is male or female writing. This paper conducts a comparison analysis to examine the effectiveness of various local binary patterns (LBPs) techniques in detecting gender from scanned images of handwriting. We study different LBP variants, including complete local binary pattern (CLBP), local ternary pattern (LTP), local configuration pattern (LCP), rotated local binary pattern (RLBP), local binary pattern variance (LBPV), and multi-scale local binary pattern (MLBP), as features for representing handwriting images. A support vector machine (SVM) is trained using features from male and female writing. The method achieves encouraging classification rates of 76.68 when tested on subsets of the Qatar University writer identification (QUWI) dataset containing English and Arabic writing samples when using the experimental protocols of the International Conference on Document Analysis and Recognition (ICDAR) 2013 gender classification competitions.