A Loss-initiated GAN-based Convolutional LSTM Method for Compression and Motion Estimation-Based Objective Enhancement in Images and Videos

Abstract

The issues of finding a suitable loss function for perceived similarity and enhancing perceptual quality in substantially compressed videos still need to be resolved. The LIGAN-Conv-LSTM is a convolutional long-short-term memory system that integrates a loss-initialised generative adversarial network. This system was developed to address the challenge of defining unified training objectives that improve both rough and smooth content. The goal is to enhance compression quality by employing advanced feature selection and prediction techniques. The study begins by presenting a comprehensive examination of multimedia, emphasising its diverse attributes, encompassing retention, awareness, conversation, and knowledge. The research employs Shape Local Binary Texture (SLBT), Overlapped Segmentation-based Independent Component Analysis (OS-ICA), and Scale-Invariant Feature Transform (SIFT) as approaches to extract useful features. Furthermore, this research acknowledges the necessity for enhanced motion estimates. To address this, a hybrid technique is proposed to integrate the most advantageous elements of coyote and tuna swarm optimisation. The objective is to enhance the accessibility of feature selection, a crucial factor in improving classifier performance. The primary contribution of the research involves replacing standard motion estimating algorithms with a Conv-LSTM network, enabling the prediction of concise latent representations for future frames. The study's results emphasise the efficacy of the proposed hybrid search algorithm in achieving a high level of matching accuracy, as demonstrated by testing on a representative collection of video clips. The planned task's accuracy level is 97.73, demonstrating its cost-effective productivity.