All-Optical DCT Encoding and Information Compression Based on Diffraction Neural Network
He Ren, YuXiang Feng, Shuai Zhou, Di Wang, Xu Yang, Shouqian Chen · ACS Photonics · 2025
As information technology advances and data volumes grow rapidly, there is an increasing requirement for information security and throughput in image transmission systems. Diffractive neural networks (DNNs), a novel all-optical information processing paradigm, offer several advantages including high-speed processing, low energy consumption, and high spatial utilization. These networks also leverage the powerful reverse-design capabilities of deep learning methods, enabling the efficient implementation of various image information encoding techniques. The discrete cosine transform (DCT), a well-established technology widely used in image encoding, shares features with DNNs, such as linear operations, spatial–frequency domain conversions, and high parallelism. This research focuses on building an all-optical DCT processor based on the DNN architecture (DCT–DNN). Testing revealed that this processor performed DCT operations on random matrices and achieved DCT-based compression on specific data sets. Additionally, the DCT with a block for large-sized images was validated. The DCT–DNN, with its high speed and low energy consumption, can be integrated with other complex optoelectronic computing systems to serve as a general computing device for computational acceleration. Furthermore, it can be combined with data transmission systems or directly integrated into image information collection systems to encode and transmit front-end collected information. This makes it a valuable tool for data processing, encryption, and transmission applications.