No-Reference Image Quality Assessment through Interactive Neuroevolution
نویسندگان
چکیده
Image quality assessment is a complex problem due to subjective nature of human visual perception. One of possible ways to take into consideration user’s subjectivism is to develop interactive system which could learn the properties of user’s visual perception. In this paper we present a novel way to do this via interactive neuroevolution approach. The key feature of this approach is training of artificial neural network to evaluate image quality on the base of interaction with user. Obtained solutions can then be used for image quality assessment for various automated image and video processing techniques.
منابع مشابه
A Machine Learning Approach to No-Reference Objective Video Quality Assessment for High Definition Resources
The video quality assessment must be adapted to the human visual system, which is why researchers have performed subjective viewing experiments in order to obtain the conditions of encoding of video systems to provide the best quality to the user. The objective of this study is to assess the video quality using image features extraction without using reference video. RMSE values and processing ...
متن کاملPicbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space
For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potentia...
متن کاملReduced-Reference Image Quality Assessment based on saliency region extraction
In this paper, a novel saliency theory based RR-IQA metric is introduced. As the human visual system is sensitive to the salient region, evaluating the image quality based on the salient region could increase the accuracy of the algorithm. In order to extract the salient regions, we use blob decomposition (BD) tool as a texture component descriptor. A new method for blob decomposition is propos...
متن کاملA Novel Image Structural Similarity Index Considering Image Content Detectability Using Maximally Stable Extremal Region Descriptor
The image content detectability and image structure preservation are closely related concepts with undeniable role in image quality assessment. However, the most attention of image quality studies has been paid to image structure evaluation, few of them focused on image content detectability. Examining the image structure was firstly introduced and assessed in Structural SIMilarity (SSIM) measu...
متن کاملAutomatic no-reference image quality assessment
No-reference image quality assessment aims to predict the visual quality of distorted images without examining the original image as a reference. Most no-reference image quality metrics which have been already proposed are designed for one or a set of predefined specific distortion types and are unlikely to generalize for evaluating images degraded with other types of distortion. There is a str...
متن کامل