Blind Image Quality Assessment (BIQA) is crucial for various image processing applications, including image denoising, transmission evaluation, optimization, watermarking, and situations where a reference image is unavailable. However, existing state-of-the-art Image Quality Assessment (IQA) metrics are often specific to certain types of distortion and fail to align with human perception. To address this issue, our research study proposes a novel approach that incorporates perceptual-based features and utilizes a pooling algorithm based on Extreme Learning Machine (ELM). By considering human visual characteristics and the impact on image content, we aim to mimic human perception, which can detect noticeable differences at specific frequency ranges, akin to neurons. The work is divided into three phases. In the first phase, we derive perceptual features using lifting wavelet, focusing on texture, edge, and contrast components. Subsequently, in the second phase, these features are trained to generate output for pooling using Extreme Learning Machine (ELM). The pooling strategy of ELM is chosen due to its ability to overcome limitations found in previous pooling techniques like Neural Networks (NNs) and Support Vector Regression (SVR). This approach enables us to evaluate the quality score of images accurately, benefiting from ELM's superior learning accuracy and faster learning speed. The third phase involves performance evaluation, including statistical analysis for algorithm validation and a comparison with existing BIQA methods using MATLAB software. We verify our proposed approach on diverse image databases containing various distortion types, aiming to create a general-purpose BIQA solution. The outcomes of this work will have significant implications in several image processing applications, such as optimizing image enhancement for medical purposes like tumor or cancer detection, image watermarking for security applications, image coding and compression, and image forensic analysis. In biodiversity monitoring, image enhancement plays a crucial role in tracking and data collection.
