Video Frame Transmission Using Semantic Frame Segmentation Over Deep Series Network with AWGN Channel

Video analysis relies on segmentation of objects of interest within a video sequence. . Segmenting entities in videos is a few of the most frequently acclaimed and thoroughly explored strategies. Computer vision applications that has shown outstanding performance improvements due to advances in deep learning techniques. Using deep learning, we will present a systematic literature review of video segmentation advances, highlighting their advantages and disadvantages. The proposed method uses the semantic segmentation for the Frames obtained from video conversion. Using images from the current frame, semantic encoders are designed and processed through a serial semantic network. QAM (16) supports higher data rates and spectral efficiencies since it uses Quadrature amplitude modulation (QAM). By using Monte Carlo simulations with additive white Gaussian noise (AWGN), it is possible to calculate the bit error rate (BER) of QAM. The complexity increases with the size of the constellation. Segmentation and communication network for semantics. In addition to the effectiveness of semantic segmentation measures such as worldwide preciseness, class reliability, IoU, weighted IoU, and BF score, the Signal to Noise Ratio (SNR) and Symbol Error Rate (SER) were determined.