Scalable High Efficiency Video Coding (SHVC) is the scalable extension of the latest video coding standard H. 265/HEVC. Video rate control algorithm is out of the scope of video coding standards. Appropriate rate control algorithms are designed for various applications to overcome practical constraints such as bandwidth and buffering constraints. In most of the scalable video applications, such as video on demand (VoD) and broadcasting applications, encoded bitstreams with variable bit rates are preferred to bitstreams with constant bit rates. In variable bit rate (VBR) applications, the tolerable delay is relatively high. Therefore, we utilize a larger buffer to allow more variations in bitrate to provide smooth and high visual quality of output video. In this paper, we propose a fuzzy video rate controller appropriate for VBR applications of SHVC. A fuzzy controller is used for each layer of scalable video to minimize the fluctuation of QP at the frame level while the buffering constraint is obeyed for any number of layers received by a decoder. The proposed rate controller utilizes the well-known structural similarity index (SSIM) as a quality metric to increase the visual quality of the output video. The proposed rate control algorithm is implemented in HEVC reference software and comprehensive experiments are executed to tune the fuzzy controllers and also to evaluate the performance of the algorithm. Experimental results show a high performance for the proposed algorithm in terms of rate control, visual quality, and rate-distortion performance.

Super-resolution is a crucial task in image processing, enhancing the resolution of low-quality images for applications such as surveillance, remote sensing, and autonomous systems. Traditional methods often struggle to preserve fine details, leading to artifacts and reduced visual fidelity. This study introduces the Pretrained RU-SRGAN, an enhanced Super-Resolution Generative Adversarial Network (SRGAN) that incorporates U-Net architecture, residual learning, and autoencoder pretraining to improve both image quality and computational efficiency, particularly in resource-constrained environments like UAVs. The goal of this research is to evaluate how these architectural modifications can enhance super-resolution performance with limited data. Autoencoder pretraining enables the generator to leverage learned features from low-resolution images, accelerating convergence and improving high-resolution reconstructions. Experimental results show that Pretrained RU-SRGAN outperforms baseline models, achieving a PSNR of 25.7 dB and an SSIM of 0.83. These results highlight the model's ability to preserve fine details and structural integrity, making it particularly effective for real-time image enhancement in UAV applications. The Pretrained RU-SRGAN provides a robust solution for super-resolution tasks, balancing high-quality image reconstruction with computational efficiency, and is well-suited for practical deployment in dynamic, resource-limited environments.

A novel approach for steganography cover selection is proposed, based on image texture features and human visual system. Our proposed algorithm employs run length matrix to select a set of appropriate images from an image database and creates their stego version after embedding process. Then, it computes similarity between original images and their stego versions by using structural similarity as image quality metric to select, as the best cover, one image with maximum similarity with its stego. According to the results of comparing our new proposed cover selection algorithm with other steganography methods, it is confirmed that the proposed algorithm is able to increase the stego quality. We also evaluated the robustness of our algorithm over steganalysis methods such as Wavelet based and Block based steganalyses, the experimental results show that the proposed approach decreases the risk of message hiding detection.