## 3 Our contributions  
In this section, we will present our contributions to the development of vehicular OCC systems. Precisely, this paper provides and investigates the principle of the blurring phenomenon on an image and the impact of this phenomenon on the quality of a camera-based communication system. Currently, many studies have been carried out on blurring images, and possible solutions based on many advanced computer vision (CV) algorithms have been provided to process and recover blurry images. However, these deblurring and denoising algorithms are mostly based on convolutional NNs and have already been studied extensively (see, for example, [31; 32; 33; 34; 35; 36; 37]). Moreover, to be carried out in such a communication system, these algorithms are computationally expensive, highly complex, and time-consuming. The reason being that most of these algorithms are specifically developed for reconstructing a latent image from a degraded image that has been contaminated by the blurring process, noise, and other factors. To do that, most of the existing techniques need to pass through many post-processing steps, such as object recognition and feature extraction, which heavily rely on the input of a clear image. The number of images required for image reconstruction could be one [31; 32; 33] or multiple [34; 35; 36; 37].  
Figure 2: Reference architecture of the region-of-interest (RoI) signaling system employing DS8-PSK.  
In the OCC system, the data being transmitted using LED light and an image frame are captured using a camera. Often, the LED states can be extracted from a small partition of an image frame, which occasionally are points intensity values [8,30]. Provided that the RoI information is made available and updated from other sources, such as low exposure camera [4,8,30,38,39], to decode high-rate data, the receiver does not need to process the whole image or recover it when the blurring process appears.  
Consequently, in this paper, we propose and design an NN-based decoder for decoding data from the received image frame in the OCC receiver. Further, we shall provide a brief discussion on critical factors to be considered when designing any NN model. From that point of view, we also highlight our novel contributions on data preprocessing and input features extraction which enable the enhancement of decoder performance.  
Moreover, to the best of our knowledge, little or none has been done to analyze the performance and measure the bit or SER of a decoder of an OCC system, including implementation and simulation, over a hybrid channel of blur and noise.  
In summary, our contributions in this paper are as follows:  
* We define a novel channel model which can give the nearest approximation of the channel model in vehicular OCC system, considering the blur effect caused by a different type of environment condition, such as rainy, foggy, snowy, etc. The defined channel model is also designed to be able to simulate.
* We provide the principle of the blurred phenomenon on the image: How it affects the quality of the communication channel and the performance of the traditional decoder in an image sensor-based communication system.
* We propose a new method of using deep learning and NN to decode the high-rate OCC waveform (DS8-PSK): The model architecture and dataset preparation for training and performance testing.
* We prove the robustness of a new AI-based decoder on a novel channel model by analyzing the SER performance of each decoding method. Thus, making a performance comparison between using the traditional decoder and our proposed AI-based decoder.