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Compression of face images using meta-heuristic algorithms based on curvelet transform with variable bit allocation

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Abstract

For valuable areas of a face image (such as the eyes, nose, and mouth) that contain detailed facial information, a higher bit rate may be assigned. If we can identify areas in an image that are of low importance, we can remove redundant information to an acceptable level. Meta-heuristic algorithms are capable of solving high-dimensional problems in the shortest time by finding the best answer among all possible solutions. In this article, meta-heuristic algorithms based on the curvelet transform are used to compress face images with high spatial resolution. Genetic algorithms, whales, gray wolves, and honey badgers are responsible for identifying these edges and important areas. In order to achieve maximum recognition accuracy, average PSNR and SSIM, the bit string lengths of each block are arranged to achieve the appropriate objective function estimation. As a case study, images from the FEI database are used. The performance of the curvelet transform method in evaluating the average PSNR and SSIM in this research shows significantly better performance when compared to the wavelet transform method when using the same meta-heuristic algorithms for the recognition accuracy values in the same conditions (equal recognition accuracy). As can be seen from the graph, the Whale optimization algorithm (WOA) has achieved the optimal response in most bit rates with a faster processing speed than most other algorithms. Evaluation of the results indicates that the curvelet transform method performs better in terms of extracting and displaying important details in the edges of the face image compared to the wavelet transform method.

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  1. Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, 47148-71167, Iran

    Reza Khodadadi & Gholamreza Ardeshir

  2. Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

    Hadi Grailu

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  1. Reza Khodadadi
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Khodadadi, R., Ardeshir, G. & Grailu, H. Compression of face images using meta-heuristic algorithms based on curvelet transform with variable bit allocation. Multimedia Systems 29, 3721–3744 (2023). https://doi.org/10.1007/s00530-023-01148-0

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