Revelio: A Real-World Screen-Camera Communication System with Visually Imperceptible Data Embedding
- URL: http://arxiv.org/abs/2501.02349v1
- Date: Sat, 04 Jan 2025 18:05:08 GMT
- Title: Revelio: A Real-World Screen-Camera Communication System with Visually Imperceptible Data Embedding
- Authors: Abbaas Alif Mohamed Nishar, Shrinivas Kudekar, Bernard Kintzing, Ashwin Ashok,
- Abstract summary: We present Revelio', a real-world screen-camera communication system leveraging temporal flicker fusion in the OKLAB color space.
Revelio achieves visually imperceptible data embedding while remaining robust against noise, asynchronicity, and distortions in screen-camera channels.
- Score: 1.9924262168387745
- License:
- Abstract: We present `Revelio', a real-world screen-camera communication system leveraging temporal flicker fusion in the OKLAB color space. Using spatially-adaptive flickering and encoding information in pixel region shapes, Revelio achieves visually imperceptible data embedding while remaining robust against noise, asynchronicity, and distortions in screen-camera channels, ensuring reliable decoding by standard smartphone cameras. The decoder, driven by a two-stage neural network, uses a weighted differential accumulator for precise frame detection and symbol recognition. Initial experiments demonstrate Revelio's effectiveness in interactive television, offering an unobtrusive method for meta-information transmission.
Related papers
- SEDS: Semantically Enhanced Dual-Stream Encoder for Sign Language Retrieval [82.51117533271517]
Previous works typically only encode RGB videos to obtain high-level semantic features.
Existing RGB-based sign retrieval works suffer from the huge memory cost of dense visual data embedding in end-to-end training.
We propose a novel sign language representation framework called Semantically Enhanced Dual-Stream.
arXiv Detail & Related papers (2024-07-23T11:31:11Z) - Neuromorphic Synergy for Video Binarization [54.195375576583864]
Bimodal objects serve as a visual form to embed information that can be easily recognized by vision systems.
Neuromorphic cameras offer new capabilities for alleviating motion blur, but it is non-trivial to first de-blur and then binarize the images in a real-time manner.
We propose an event-based binary reconstruction method that leverages the prior knowledge of the bimodal target's properties to perform inference independently in both event space and image space.
We also develop an efficient integration method to propagate this binary image to high frame rate binary video.
arXiv Detail & Related papers (2024-02-20T01:43:51Z) - Information hiding cameras: optical concealment of object information
into ordinary images [11.41487037469984]
We introduce an optical information hiding camera integrated with an electronic decoder, jointly optimized through deep learning.
This information hiding-decoding system employs a diffractive optical processor as its front-end, which transforms and hides input images in the form of ordinary-looking patterns that deceive/mislead human observers.
By processing these ordinary-looking output images, a jointly-trained electronic decoder neural network accurately reconstructs the original information hidden within the deceptive output pattern.
arXiv Detail & Related papers (2024-01-15T17:37:27Z) - Aggregating Nearest Sharp Features via Hybrid Transformers for Video Deblurring [70.06559269075352]
We propose a video deblurring method that leverages both neighboring frames and existing sharp frames using hybrid Transformers for feature aggregation.
To aggregate nearest sharp features from detected sharp frames, we utilize a global Transformer with multi-scale matching capability.
Our proposed method outperforms state-of-the-art video deblurring methods as well as event-driven video deblurring methods in terms of quantitative metrics and visual quality.
arXiv Detail & Related papers (2023-09-13T16:12:11Z) - Revisiting Event-based Video Frame Interpolation [49.27404719898305]
Dynamic vision sensors or event cameras provide rich complementary information for video frame.
estimating optical flow from events is arguably more difficult than from RGB information.
We propose a divide-and-conquer strategy in which event-based intermediate frame synthesis happens incrementally in multiple simplified stages.
arXiv Detail & Related papers (2023-07-24T06:51:07Z) - Dual Memory Aggregation Network for Event-Based Object Detection with
Learnable Representation [79.02808071245634]
Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner.
Event streams are divided into grids in the x-y-t coordinates for both positive and negative polarity, producing a set of pillars as 3D tensor representation.
Long memory is encoded in the hidden state of adaptive convLSTMs while short memory is modeled by computing spatial-temporal correlation between event pillars.
arXiv Detail & Related papers (2023-03-17T12:12:41Z) - A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild [72.0226493284814]
We propose a unified framework for event-based frame that performs deblurring ad-hoc.
Our network consistently outperforms previous state-of-the-art methods on frame, single image deblurring, and the joint task of both.
arXiv Detail & Related papers (2023-01-12T18:19:00Z) - Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception [17.585862399941544]
Event cameras address limitations as they report brightness changes of each pixel independently with a fine temporal resolution.
integrated hybrid event-frame sensors (eg., DAVIS) are available, but the quality of data is compromised by coupling at the pixel level in the circuit fabrication of such cameras.
This paper proposes a stereo hybrid event-frame (SHEF) camera system that offers a sensor modality with separate high-quality pure event and pure frame cameras.
arXiv Detail & Related papers (2021-10-11T04:03:36Z) - DeepLight: Robust & Unobtrusive Real-time Screen-Camera Communication
for Real-World Displays [4.632704227272501]
DeepLight is a system that incorporates machine learning (ML) models in the decoding pipeline to achieve humanly-imperceptible, moderately high SCC rates.
DeepLight's key innovation is the design of a Deep Neural Network (DNN) based decoder that collectively decodes all the bits spatially encoded in a display frame.
arXiv Detail & Related papers (2021-05-11T14:44:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.