Enhancing Image Quality Assessment combining CNN: A Review of Methods and Applications
DOI : 10.46335/IJIES.2024.9.2.2
Authors : Manoj Lilachand Patel , Vikas Tiwari
Abstract – This paper explores Convolutional Neural Networks (CNNs) in image quality assessment (IQA), emphasizing their potential to enhance image processing across industries. Due to the limitations of conventional metrics such as peak signal-to-noise ratio (PSNR) and mean square error (MSE), CNNs are being investigated for IQA. Despite challenges like data requirements and transparency, CNN-based IQA offers significant advantages. The paper discusses how CNNs handle complex distortions and emphasizes the need for diverse evaluation metrics to assess their effectiveness. Understanding CNN-based IQA's capabilities is crucial for its integration into image processing systems.
