<p>Quantum image processing (QIP) establishes a connection between quantum computation and image processing by utilizing diverse quantum image representations. This paper presents a comprehensive review of a number of quantum image representation techniques, categorizing them into four broad models, namely precision amplitude representation, threshold intensity representation, probabilistic state representation, and phase-based intensity representation. This paper further evaluated these QIR models based on several critical performance analyses, including the quantum circuit depth, the number of qubits employed, the time complexity associated with each model, entropy, and information losses. We have also highlighted state-of-the-art studies that have addressed QIR and its applications in various fields of image processing. This paper concludes by illuminating the strengths and limitations of each QIR technique which can lay the basis for future work to obtain better QIP algorithms. As a result, it paves the way for more efficient and powerful quantum computing applications in image processing and beyond.</p>