This paper provides a comprehensive survey of image encryption algorithms and their evaluation metrics. It classifies image encryption into seven approaches: traditional ciphers, chaotic systems, DNA encoding, neural networks, compressive sensing, frequency domain, and meaningful sensing. The paper analyzes the strengths and weaknesses of each approach and discusses security, quality, and efficiency evaluation metrics such as correlation coefficient, histogram analysis, entropy, mean square error (MSE), and the NIST SP 800-22 test. It also provides upper and lower bounds for these metrics and discusses the pros and cons of different image encryption approaches, as well as their suitability for various applications. The paper reviews various image encryption algorithms, including those based on logistic maps, Baker maps, Arnold maps, tent maps, Henon maps, hyperchaotic systems, and multiple chaotic maps. It also explores DNA encoding, neural networks, frequency domain, compressive sensing, and meaningful encryption techniques. The paper concludes with a discussion of evaluation metrics used to assess image encryption algorithms, including correlation coefficient analysis, histogram analysis, and other statistical tests. The study aims to provide researchers and practitioners with a clear understanding of the current state of image encryption algorithms and their evaluation metrics.This paper provides a comprehensive survey of image encryption algorithms and their evaluation metrics. It classifies image encryption into seven approaches: traditional ciphers, chaotic systems, DNA encoding, neural networks, compressive sensing, frequency domain, and meaningful sensing. The paper analyzes the strengths and weaknesses of each approach and discusses security, quality, and efficiency evaluation metrics such as correlation coefficient, histogram analysis, entropy, mean square error (MSE), and the NIST SP 800-22 test. It also provides upper and lower bounds for these metrics and discusses the pros and cons of different image encryption approaches, as well as their suitability for various applications. The paper reviews various image encryption algorithms, including those based on logistic maps, Baker maps, Arnold maps, tent maps, Henon maps, hyperchaotic systems, and multiple chaotic maps. It also explores DNA encoding, neural networks, frequency domain, compressive sensing, and meaningful encryption techniques. The paper concludes with a discussion of evaluation metrics used to assess image encryption algorithms, including correlation coefficient analysis, histogram analysis, and other statistical tests. The study aims to provide researchers and practitioners with a clear understanding of the current state of image encryption algorithms and their evaluation metrics.