ANALYSIS AND PREDICTION OF FILTERING EFFICIENCY USING NO-REFERENCE IMAGE VISUAL QUALITY METRICS

Abstract

Images are subject to noise during acquisition, transmission and processing. Image denoising is highly desirable, not only to provide better visual quality, but also to improve performance of the subsequent operations such as compression, segmentation, classification, object detection and recognition. In the past decades, a large number of image denoising algorithms has been developed, ranging from simple linear methods to complex methods based on similar blocks search and deep convolutional neural networks. However, most of existing denoising techniques have a tendency to oversmooth image edges, fine details and textures. Thus, there are cases when noise reduction leads to loss of image features and filtering does not produce better visual quality. According to this, it is very important to evaluate denoising result and hence to undertake a decision whether denoising is expedient. Despite the fact that image denoising has been one of the most active research areas, only a little work has been dedicated to visual quality evaluation for denoised images. There are many approaches and metrics to characterize image quality, but adequateness of these metrics is of question. Existing image quality metrics, especially no-reference ones, have not been thoroughly studies for image denoising. In terms of using visual quality metrics, it is usually supposed that the higher the improvement for a given metric, the better visual quality for denoised image. However, there are situations when denoising does not result in visual quality enhancement, especially for texture images. Thus, it would be desirable to predict human subjective evaluation for denoised image. Then, this information will clarify when denoising can be expedient. The purpose of this paper is to give analysis of denoising expedience using no-reference (NR) image quality metrics. In addition, this work considers possible ways to predict human subjective evaluation of denoised images based on several input parameters. More in details, two denoising techniques, namely the standard sliding window DCT filter and the BM3D filter have been considered. Using a specialized database of test images SubjectiveIQA, performance evaluation of existing state-of-the-art objective no-reference quality metrics for denoised images is carried out

Keywords

• эффективность фильтрации
• безэталонные меры визуального качества изображений
• прогнозирование
• дкп фильтр
• bm3d
• аддитивные помехи
• субъективный эксперимент