This article explores nonlinear encoding strategies in diffractive optical processors using linear optical materials, comparing their performance with digital deep neural networks. The study analyzes different nonlinear encoding methods, including phase encoding and data repetition-based encoding, to evaluate their effectiveness in tasks like image classification. It shows that phase encoding without data repetition provides a simpler and comparable statistical inference accuracy to data repetition-based methods. Data repetition-based diffractive blocks, while losing universal linear transformation capability, can still achieve enhanced accuracy for specific tasks. The study also highlights that phase encoding allows for all-optical processing of phase-only input data without the need for digital preprocessing. The results demonstrate that data repetition-based diffractive processors can improve inference accuracy, though they are less universal than non-repetition methods. The study compares various encoding strategies using datasets like MNIST, Fashion-MNIST, and CIFAR-10, showing that phase encoding achieves high accuracy. It also discusses the advantages of differential detection schemes and hybrid systems combining diffractive and electronic components. The findings suggest that while data repetition-based methods have limitations, they can still offer performance benefits in specific applications. The study emphasizes the importance of understanding the trade-offs between different encoding strategies in diffractive optical processing.This article explores nonlinear encoding strategies in diffractive optical processors using linear optical materials, comparing their performance with digital deep neural networks. The study analyzes different nonlinear encoding methods, including phase encoding and data repetition-based encoding, to evaluate their effectiveness in tasks like image classification. It shows that phase encoding without data repetition provides a simpler and comparable statistical inference accuracy to data repetition-based methods. Data repetition-based diffractive blocks, while losing universal linear transformation capability, can still achieve enhanced accuracy for specific tasks. The study also highlights that phase encoding allows for all-optical processing of phase-only input data without the need for digital preprocessing. The results demonstrate that data repetition-based diffractive processors can improve inference accuracy, though they are less universal than non-repetition methods. The study compares various encoding strategies using datasets like MNIST, Fashion-MNIST, and CIFAR-10, showing that phase encoding achieves high accuracy. It also discusses the advantages of differential detection schemes and hybrid systems combining diffractive and electronic components. The findings suggest that while data repetition-based methods have limitations, they can still offer performance benefits in specific applications. The study emphasizes the importance of understanding the trade-offs between different encoding strategies in diffractive optical processing.