# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 1 Introduction  
With the recent advancements in display technology, widespread availability of cameras in mobiles, laptops, augmented/virtual reality (AR/VR) devices, etc., and increasing consumption of digital content, display-to-camera (D2C) communications [1; 2; 3] will soon become an important part of future wireless communication systems. D2C communication is an optical camera communication technology, where an electronic display and a digital camera communicate via a wireless D2C link. In particular, the data are embedded into the individual video frames at the display (i.e., the transmitter). On the receiver end, a camera captures the frames and decodes the embedded data. In this way, the display serves both as the data transmitter as well as a screen showing contents to normal audiences. D2C communication can be related to currently used quick response (QR) codes (or 2D barcodes) that transmit a very small amount of data to a mobile camera device [4; 5; 6]. In addition, even though QR codes can be made imperceptible to the human eye [7; 8; 9], they are limited by their size, position, and the number of bits transmitted. On the other hand, D2C communication considers embedding data into a full video stream while remaining unobtrusive to the human eye. That is, it will not obstruct the normal viewing experience of the public while the interested users can get the related side-information of the ongoing content on their mobiles. The most prominent application of D2C communication could be in the field of digital signage, e.g., in hospitals, education, and the hospitality sector,among others. Alternative applications could be in the fields of human-computer interface, AR/VR, blockchain-based non-fungible tokens, and cybersecurity and privacy [2]. Display Field Communication (DFC) is a kind of D2C communication, where the data are embedded in the frequency domain of an image, rather than the spatial domain [10]. This is done so that the properties associated with the frequency coefficients of an image can be employed. In addition, this will reduce the visual artifacts of an image frame to the naked eye. The pioneering work of [10] proposed and analyzed the DFC technology, where the data are embedded in one dimension of an image frame. The work was then extended to the 2D-DFC [11], where the data are embedded in two dimensions of an image frame ultimately increasing the achievable data rate. The work in [12] showed that the achievable data rates of 1D-DFC could further be improved using advanced digital receivers. However, all the works remain limited to the analytical and simulations stage. In this paper, we make use of machine learning (ML) [13, 14, 15, 16, 17] skills in the context of DFC. Moreover, we perform several practical lab tests to validate the DFC and how ML can be employed to improve its performance. First of all, we adopted Discrete Cosine Transform (DCT) as the frequency conversion method for the input images. The DCT is one of the most widely used frequency conversion methods and is already used in compressions such as JPEG and MPEG. After converting the image to the frequency domain, we do data insertion (or data embedding) through an _addition allocator_. Among various data-embedding mechanisms [18], we have chosen addition to reduce the mathematical complexity of the proposed scheme. After the data embedding, the spectral-domain image is converted back to the spatial domain and displayed on an electronic screen. At the receiver end, a camera captures the image and decodes the transmitted data. The main contributions of the manuscript are summarized as follows:  
* First of all, we have proved that DFC is possible in a real-life scenario. Unlike previous studies, where we have mostly developed simulations and algorithms to study DFC, we have performed a practical indoor experiment to check the feasibility of the DFC.