NIMA: Neural Image Assessment Hossein Talebi and Peyman Milanfar This paper presents a novel approach for automatically assessing the quality and aesthetics of images using deep learning. The proposed method predicts the distribution of human opinion scores using a convolutional neural network (CNN), rather than just the mean opinion score as in most existing methods. The approach leverages pre-trained deep object recognition networks and is significantly simpler than other methods with comparable performance. The resulting network can reliably score images with high correlation to human perception and assist in adapting and optimizing photo editing/enhancement algorithms in a photographic pipeline. The method does not require a "golden" reference image, allowing for single-image, semantic- and perceptually-aware, no-reference quality assessment. The paper discusses the challenges of image quality assessment, including the subjective nature of the problem and the need for models that can predict both technical and aesthetic qualities. It reviews three widely used datasets for quality assessment: AVA, TID2013, and LIVE. The AVA dataset contains 255,000 images rated by amateur photographers, while TID2013 contains 3000 images with various distortions. LIVE contains 1162 photos rated by 175 unique subjects. The proposed method uses a CNN architecture, including VGG16, Inception-v2, and MobileNet, to predict the distribution of ratings for a given image. The method uses a squared Earth Mover's Distance (EMD) loss to train the model, which shows improved performance in classification with ordered classes. The model is trained on different datasets and achieves state-of-the-art performance for both technical and aesthetic quality assessment. The paper also discusses the application of the proposed method in photo ranking and image enhancement. The model can be used to tune parameters of image enhancement operators to produce perceptually superior results. The method is evaluated on various datasets, showing high correlation with ground truth ratings and outperforming existing methods in terms of accuracy and correlation. The results show that the proposed method can effectively predict the distribution of ratings for images, leading to more accurate quality assessment. The model is also able to guide image enhancement algorithms to produce better results. The paper concludes that the proposed method is a promising approach for image quality assessment and has potential applications in various image processing tasks.NIMA: Neural Image Assessment Hossein Talebi and Peyman Milanfar This paper presents a novel approach for automatically assessing the quality and aesthetics of images using deep learning. The proposed method predicts the distribution of human opinion scores using a convolutional neural network (CNN), rather than just the mean opinion score as in most existing methods. The approach leverages pre-trained deep object recognition networks and is significantly simpler than other methods with comparable performance. The resulting network can reliably score images with high correlation to human perception and assist in adapting and optimizing photo editing/enhancement algorithms in a photographic pipeline. The method does not require a "golden" reference image, allowing for single-image, semantic- and perceptually-aware, no-reference quality assessment. The paper discusses the challenges of image quality assessment, including the subjective nature of the problem and the need for models that can predict both technical and aesthetic qualities. It reviews three widely used datasets for quality assessment: AVA, TID2013, and LIVE. The AVA dataset contains 255,000 images rated by amateur photographers, while TID2013 contains 3000 images with various distortions. LIVE contains 1162 photos rated by 175 unique subjects. The proposed method uses a CNN architecture, including VGG16, Inception-v2, and MobileNet, to predict the distribution of ratings for a given image. The method uses a squared Earth Mover's Distance (EMD) loss to train the model, which shows improved performance in classification with ordered classes. The model is trained on different datasets and achieves state-of-the-art performance for both technical and aesthetic quality assessment. The paper also discusses the application of the proposed method in photo ranking and image enhancement. The model can be used to tune parameters of image enhancement operators to produce perceptually superior results. The method is evaluated on various datasets, showing high correlation with ground truth ratings and outperforming existing methods in terms of accuracy and correlation. The results show that the proposed method can effectively predict the distribution of ratings for images, leading to more accurate quality assessment. The model is also able to guide image enhancement algorithms to produce better results. The paper concludes that the proposed method is a promising approach for image quality assessment and has potential applications in various image processing tasks.