<p>The Versatile Video Coding (VVC) shows better performance by combining various functions and features for the high dynamic range, and better spatial resolution achieving better bit rate savings than the existing video coding models. Although the VVC maintains better quality compressed video utilizing extra encoding functions, the VVC is still in the operation of continuous enhancement, and the experts are continuously suggesting new technologies to enhance the VVC's coding performance. In addition, the traditional mechanisms utilize more resources and the encoding time to perform the task. Since the conventional models are normally complex and the parameter’s amount is highly large, it is needed to develop a lightweight model for VVC. Hence, a new mechanism is suggested in this work for video compression and bit rate minimization based on VVC by influencing deep learning models. At first, by employing the Motion Vector (MV) encoder-decoder task, the motion is measured in the suggested work. Moreover, with the assistance of this MV, the frame reformation is carried out to conduct the motion compensation. The compression process is performed and the residual images are achieved by adopting the Vision Transformer-based Adaptive Residual Attention DenseNet (ViT-ARADNet), where the parameters included in this network are optimally tuned by the Random Value Enhanced Pelican Optimization (RVEPO). Further, the bit rate of the residual image is determined by the entropy coding in the presented work’s training phase. Subsequently, the video quality assessment metrics such as Visual Information Fidelity (VIF) and predicted Differential Mean Opinion Score (DMOSp) are measured to enrich the model functionality.</p>