Crow Search Algorithm Based Vector Quantization Approach for Image Compression in 6G Enabled Industrial Internet of Things Environment

The next revolution of industrial internet of things (IIoT) has gained considerable interest due to the advances in 6G networks and Internet of Things (IoT). Since the IIoT generates massive quantity of images, an effective approach is needed to store the data in a compact way. One of the efficient solutions in image compression is lessening the quantity of data storage and communication. This study introduces a novel crow search algorithm based vector quantization approach for image compression in 6G enabled IIoT environment, called CSAVQ-ICIIoT model. The proposed CSAVQ-ICIIoT model intends to accomplish effectual image compression by optimizing codebook construction process in 6G enabled IIoT platform. The CSAVQ-ICIIoT technique includes Linde–Buzo–Gray (LBG) with vector quantization (VQ) technique for image compression. Besides, the optimal codebook construction process is performed by the use of crow search algorithm (CSA). For examining the improved performance of the CSAVQ-ICIIoT model, a detailed result analysis is made and the results are inspected under several measures. The experimental results reported the enhanced outcomes of the CSAVQ-ICIIoT model over the other methods.