A Fast Quantum Image Compression Algorithm based on Taylor Expansion
This work addresses image storage efficiency for high-resolution image processing applications, representing an incremental improvement over previous quantum compression methods.
The paper tackles the challenge of balancing compressed size and image quality in image compression by upgrading a quantum image compression algorithm using parameterized quantum circuits and first-order Taylor expansion, achieving up to 86% reduction in iterations while maintaining lower compression loss on benchmark images like Lenna and Cameraman.
With the increasing demand for storing images, traditional image compression methods face challenges in balancing the compressed size and image quality. However, the hybrid quantum-classical model can recover this weakness by using the advantage of qubits. In this study, we upgrade a quantum image compression algorithm within parameterized quantum circuits. Our approach encodes image data as unitary operator parameters and applies the quantum compilation algorithm to emulate the encryption process. By utilizing first-order Taylor expansion, we significantly reduce both the computational cost and loss, better than the previous version. Experimental results on benchmark images, including Lenna and Cameraman, show that our method achieves up to 86\% reduction in the number of iterations while maintaining a lower compression loss, better for high-resolution images. The results confirm that the proposed algorithm provides an efficient and scalable image compression mechanism, making it a promising candidate for future image processing applications.