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.

Local scouring around bridge piers had been considered as an undesirable event and against the bridge safety. In this process, the 3D flow patterns in the vicinity of bridge pier have important role and cause the bed material erosion around bridge pier. Embedment of the foundation in the depth deeper than the maximum local scour depth according to the design flood has been proposed as a conservative solution. However, implementation of the pier foundation in a deep depth from the original bed level is not an economic solution in practical cases. In real cases, the variant stream flow of floods is the main reason of bridge failures. In this study the evolution of local scour depth around circular bridge piers under unsteady flow condition of hydrographs has been numerically simulated using steady flows. The results showed that during the rising period of hydrograph, the scour depth increased steadily and more than 70% of final scour depth occurred at the rising limb of hydrograph. The results also show that by using the SSIIM program, it will be possible to evaluate the local scour depth in the end of rising limb of hydrograph with a satisfying accuracy.

Introduction: Lack of uniform light radiation on the objects, reduces the amount of contrast in the images and makes it difficult to extract image features. This problem destroys information about the behavior, shape, size, pattern, texture, and tone of the effects, and compresses the image histogram in one or more specific areas. UAV images have been widely used in recent years due to their extensive coverage, high operating speed, use in hard-to-reach areas and up-to-date equipment. If drone images are correctly taken and pre-processed, they provide good accuracy for a variety of applications. The preprocessing is important since the image acquisition conditions cannot be changed in most cases so that the acquired images are contaminated with some distortions or errors which must be removed or their effect reduced to a minimum before any process. Improving the exposure in the image, which increases the amplitude of the histogram, can highlight features with similar gray-scale values, and this is useful in identification. Materials & Methods: In this study, two aerial images have been used with a variety of vegetation, soil and man-made features using Storm 2 hexacopter drone in Simorgh city (Kiakla) in Mazandaran province with longitude and latitude 52⸰ 54' 1'' and 36⸰ 35' 49''. At first the SMQT algorithm is applied to the input images. So the bits number of the input image is calculated to determine the number of transmission levels. Then with rgb2gray command creates a gray image of the original image. The overall average of the image is calculated and the DN of each pixel is compared to the average. If the DN is greater than the pixel value, the number 1 is assigned to the pixel, otherwise the number zero in another image is assigned to the pixel. The average calculation and segmentation of pixels based on the number of bits continues, each segmentation is called a transfer. Then, by converting the data from these divisions into values in the spectral range of the image, a new image is created. This image has higher radiometric resolution than the original input image but lower spectral resolution. For this reason, the image is fused. Global gamma correction is applied to the fused image. Finding gamma in the image, especially local gamma is time consuming and complex for programming and computing. Therefore, to increase the computing speed, a local gamma of 0. 7 was applied to the whole image and then the first step processes are applied again and finally, the SSIM index is checked for image enhancement. Results & Discussion: The SSIM value for input image 1 and 2 is 0. 8372 and 0. 8401 while this value before processing was 0. 4352 and 0. 4161. Examining the histogram of the images before and after processing, in all three bands R, G and B, shows the stretch of the image histogram in the range of 0 to 255. There is a decrease in the number of peaks and valleys in the histogram of the processed images. The density function for input and processed images shows that the more homogeneous the number of effects in the image, the greater the slope of the function graph. The value of the density function has increased after processing, which is due to the stretching of the image histogram. SSIM is used to validate the results in this study. The images have been visually improved significantly, but this is not enough for verification. The goal of quantitative quality recognition is to design computational methods that can accurately and automatically express image quality, which affects all the image pixels in the same way. The SSIM range is between (+1 and 0). The closer the measured value for an image to one, the better image quality will be. SMQT also has less computational complexity and less configuration. If the image of a light object is formed in a completely dark background (such as night shooting), this algorithm does not work in the background pixels. Examining the image samples taken from a complication at night, it was found that the black pixels changed color to purple after fusion. In order to optimize the algorithm, it is suggested to increase the efficiency of the algorithm by examining the spectral behavior of different features in different color spaces and integrating their effective components in image or feature highlighting or the use of plant or soil indicators. The fuzzy method can also be used for semi-shady areas. These improvements should also prevent complexity of computing by increasing efficiency.

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.

Introduction: data security has become a serious concern. One of the methods against these risks is the utilization of data embedding. The patient’, s electronic record is embedded into the medical image without changing the shape and size of the image. Methods: The EPR data embedding is based on SSIM and HVS criteria employing MRI image segmentation. The idea behind two-dimensional wavelet-cosine conversion based on EPR data latency is that the final image will provide more resilience and stability against malicious attacks. Results: The present study aimed to study the effect of the proposed method, including innovations in the HVS block based on SSIM criteria for transparency and flexibility. The latency coefficient K is considered to be comparatively proportional to the N-ROI uniformity. The selection of α, f, k coefficients includes the frequency and spatial sensitivity optimization function in an adaptive way to match the visual perception of vision. Conclusion: Security in health information systems has its own weak points, planning to secure access to patients and risk management is crucial, and necessary. Research improve the level of security, confidentiality, and integrated storage of patient information and image. The simulation results, considering the parameters of robustness and transparency in comparison with other methods, were carried out with the evaluation criteria, including MSE, NC, SSIM, and BER measurements.