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The paper introduces a novel, holistic approach for robust Screen-CameraCommunication (SCC), where video content on a screen is visually encoded in ahuman-imperceptible fashion and decoded by a camera capturing images of suchscreen content. We first show that state-of-the-art SCC techniques have two keylimitations for in-the-wild deployment: (a) the decoding accuracy drops rapidlyunder even modest screen extraction errors from the captured images, and (b)they generate perceptible flickers on common refresh rate screens even withminimal modulation of pixel intensity. To overcome these challenges, weintroduce DeepLight, a system that incorporates machine learning (ML) models inthe decoding pipeline to achieve humanly-imperceptible, moderately high SCCrates under diverse real-world conditions. Deep-Light's key innovation is thedesign of a Deep Neural Network (DNN) based decoder that collectively decodesall the bits spatially encoded in a display frame, without attempting toprecisely isolate the pixels associated with each encoded bit. In addition,DeepLight supports imperceptible encoding by selectively modulating theintensity of only the Blue channel, and provides reasonably accurate screenextraction (IoU values >= 83%) by using state-of-the-art object detection DNNpipelines. We show that a fully functional DeepLight system is able to robustlyachieve high decoding accuracy (frame error rate < 0.2) and moderately-highdata goodput (>=0.95Kbps) using a human-held smartphone camera, even overlarger screen-camera distances (approx =2m).

DeepLight: Robust & Unobtrusive Real-time Screen-Camera Communication for Real-World Displays