A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

High-resolution video transmission requires a substantial amount of bandwidth.In this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-resolution enhancement.Our method commences with the accurate detection of ROIs within video sequences,followed by the application of advanced super-resolution techniques to these areas,thereby preserving visual quality while economizing on data transmission.To validate and benchmark our approach,we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications.The proposed model architecture leverages the transformer network framework,guided by a carefully designed multi-task loss function,which facilitates concurrent learning and execution of both ROI identification and resolution enhancement tasks.This unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom dataset.The implications of this research extend to optimizing low-bitrate video streaming scenarios.By selectively enhancing the resolution of critical regions in videos,our solution enables high-quality video delivery under constrained bandwidth conditions.Empirical results demonstrate a 15%reduction in transmission bandwidth compared to traditional super-resolution based compression methods,without any perceivable decline in visual quality.This work thus contributes to the advancement of video compression and enhancement technologies,offering an effective strategy for improving digital media delivery efficiency and user experience,especially in bandwidth-limited environments.The innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems.

Automated patellar height assessment on high-resolution radiographs with a novel deep learning-based approach

BACKGROUND Artificial intelligence and deep learning have shown promising results in medical imaging and interpreting radiographs.Moreover,medical community shows a gaining interest in automating routine diagnostics issues and orthopedic measurements.AIM To verify the accuracy of automated patellar height assessment using deep learning-based bone segmentation and detection approach on high resolution radiographs.METHODS 218 Lateral knee radiographs were included in the analysis.82 radiographs were utilized for training and 10 other radiographs for validation of a U-Net neural network to achieve required Dice score.92 other radiographs were used for automatic(U-Net)and manual measurements of the patellar height,quantified by Caton-Deschamps(CD)and Blackburne-Peel(BP)indexes.The detection of required bones regions on high-resolution images was done using a You Only Look Once(YOLO)neural network.The agreement between manual and automatic measurements was calculated using the interclass correlation coefficient(ICC)and the standard error for single measurement(SEM).To check U-Net's generalization the segmentation accuracy on the test set was also calculated.RESULTS Proximal tibia and patella was segmented with accuracy 95.9%(Dice score)by U-Net neural network on lateral knee subimages automatically detected by the YOLO network(mean Average Precision mAP greater than 0.96).The mean values of CD and BP indexes calculated by orthopedic surgeons(R#1 and R#2)was 0.93(±0.19)and 0.89(±0.19)for CD and 0.80(±0.17)and 0.78(±0.17)for BP.Automatic measurements performed by our algorithm for CD and BP indexes were 0.92(±0.21)and 0.75(±0.19),respectively.Excellent agreement between the orthopedic surgeons’measurements and results of the algorithm has been achieved(ICC>0.75,SEM<0.014).CONCLUSION Automatic patellar height assessment can be achieved on high-resolution radiographs with the required accuracy.Determining patellar end-points and the joint line-fitting to the proximal tibia joint surface allows for accurate CD and BP index calculations.The obtained results indicate that this approach can be valuable tool in a medical practice.