Efficiency Increasing of No‐Reference Image Quality Assessment in UAV Applications

Unmanned aerial vehicle (UAV) imaging is a dynamically developing field, where the effectiveness of imaging applications highly depends on quality of the acquired images. No-reference image quality assessment is widely used for quality control and image processing management. However, there is a lack of accuracy and adequacy of existing quality metrics for human visual perception. In this paper, we demonstrate that this problem persists for typical applications of UAV images. We present a methodology to improve the efficiency of visual quality assessment by existing metrics for images obtained from UAVs, and introduce a method of combining quality metrics with the optimal selection of the elementary metrics used in this combination. A combined metric is designed based on a neural network trained to utilize subjective assessments of visual quality. The metric was tested using the TID2013 image database and a set of real UAV images with embedded distortions. Verification results have demonstrated the robustness and accuracy of the proposed metric.Peer reviewe

Color Image Database HTID for Verification of No-Reference Metrics : Peculiarities and Preliminary Results

The paper describes a new image database HTID for verification and training of no-reference image visual quality metrics. The database contains 2880 color images of size 1536×1024 pixels cropped from the real-life photos produced by the mobile phone cameras with various shooting and post-processing settings. Mean opinion scores for images of the database are obtained. Peculiarities of the database are considered. A comparative analysis of the state-of-The-Art no-reference image visual quality metrics is carried out. It is shown that the proposed database takes its own unique place in the existing image databases and can be effectively used for metrics' verification.acceptedVersionPeer reviewe

Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing Images

Remote sensing images are subject to different types of degradations. The visual quality of such images is important because their visual inspection and analysis are still widely used in practice. To characterize the visual quality of remote sensing images, the use of specialized visual quality metrics is desired. Although the attempts to create such metrics are limited, there is a great number of visual quality metrics designed for other applications. Our idea is that some of these metrics can be employed in remote sensing under the condition that those metrics have been designed for the same distortion types. Thus, image databases that contain images with types of distortions that are of interest should be looked for. It has been checked what known visual quality metrics perform well for images with such degradations and an opportunity to design neural network-based combined metrics with improved performance has been studied. It is shown that for such combined metrics, their Spearman correlation coefficient with mean opinion score exceeds 0.97 for subsets of images in the Tampere Image Database (TID2013). Since different types of elementary metric pre-processing and neural network design have been considered, it has been demonstrated that it is enough to have two hidden layers and about twenty inputs. Examples of using known and designed visual quality metrics in remote sensing are presented

Combined No-Reference Image Quality Metric for UAV Applications

International audienc

Full-reference quality metric based on neural network to assess the visual quality of remote sensing images

Remote sensing images are subject to different types of degradations. The visual quality of such images is important because their visual inspection and analysis are still widely used in practice. To characterize the visual quality of remote sensing images, the use of specialized visual quality metrics is desired. Although the attempts to create such metrics are limited, there is a great number of visual quality metrics designed for other applications. Our idea is that some of these metrics can be employed in remote sensing under the condition that those metrics have been designed for the same distortion types. Thus, image databases that contain images with types of distortions that are of interest should be looked for. It has been checked what known visual quality metrics perform well for images with such degradations and an opportunity to design neural network-based combined metrics with improved performance has been studied. It is shown that for such combined metrics, their Spearman correlation coefficient with mean opinion score exceeds 0.97 for subsets of images in the Tampere Image Database (TID2013). Since different types of elementary metric pre-processing and neural network design have been considered, it has been demonstrated that it is enough to have two hidden layers and about twenty inputs. Examples of using known and designed visual quality metrics in remote sensing are presented.publishedVersionPeer reviewe

Quality Control for the BPG Lossy Compression of Three-Channel Remote Sensing Images

This paper deals with providing the desired quality in the Better Portable Graphics (BPG)-based lossy compression of color and three-channel remote sensing (RS) images. Quality is described by the Mean Deviation Similarity Index (MDSI), which is proven to be one of the best metrics for characterizing compressed image quality due to its high conventional and rank-order correlation with the Mean Opinion Score (MOS) values. The MDSI properties are studied and three main areas of interest are determined. It is shown that quite different quality and compression ratios (CR) can be observed for the same values of the quality parameter Q that controls compression, depending on the compressed image complexity. To provide the desired quality, a modified two-step procedure is proposed and tested. It has a preliminary stage carried out offline (in advance). At this stage, an average rate-distortion curve (MDSI on Q) is obtained and it is available until the moment when a given image has to be compressed. Then, in the first step, an image is compressed using the starting Q determined from the average rate-distortion curve for the desired MDSI. After this, the image is decompressed and the produced MDSI is calculated. In the second step, if necessary, the parameter Q is corrected using the average rate-distortion curve, and the image is compressed with the corrected Q. Such a procedure allows a decrease in the MDSI variance by around one order after two steps compared to variance after the first step. This is important for the MDSI of approximately 0.2–0.25 corresponding to the distortion invisibility threshold. The BPG performance comparison to some other coders is performed and examples of its application to real-life RS images are presented

Combined no-reference IQA metric and its performance analysis

The problem of increasing efficiency of blind image quality assessment is considered. No-reference image quality metrics both independently and as components of complex image processing systems are employed in various application areas where images are the main carriers of information. Meanwhile, existing no-reference metrics have a significant drawback characterized by a low adequacy to image perception by human visual system (HVS). Many well-known no-reference metrics are analyzed in our paper for several image databases. A method of combining several no-reference metrics based on artificial neural networks is proposed based on multi-database verification approach. The effectiveness of the proposed approach is confirmed by extensive experiments.publishedVersionPeer reviewe

Quality Control for the BPG Lossy Compression of Three-Channel Remote Sensing Images

This paper deals with providing the desired quality in the Better Portable Graphics (BPG)-based lossy compression of color and three-channel remote sensing (RS) images. Quality is described by the Mean Deviation Similarity Index (MDSI), which is proven to be one of the best metrics for characterizing compressed image quality due to its high conventional and rank-order correlation with the Mean Opinion Score (MOS) values. The MDSI properties are studied and three main areas of interest are determined. It is shown that quite different quality and compression ratios (CR) can be observed for the same values of the quality parameter Q that controls compression, depending on the compressed image complexity. To provide the desired quality, a modified two-step procedure is proposed and tested. It has a preliminary stage carried out offline (in advance). At this stage, an average rate-distortion curve (MDSI on Q) is obtained and it is available until the moment when a given image has to be compressed. Then, in the first step, an image is compressed using the starting Q determined from the average rate-distortion curve for the desired MDSI. After this, the image is decompressed and the produced MDSI is calculated. In the second step, if necessary, the parameter Q is corrected using the average rate-distortion curve, and the image is compressed with the corrected Q. Such a procedure allows a decrease in the MDSI variance by around one order after two steps compared to variance after the first step. This is important for the MDSI of approximately 0.2–0.25 corresponding to the distortion invisibility threshold. The BPG performance comparison to some other coders is performed and examples of its application to real-life RS images are presented

A New Color Image Database TID2013: Innovations and Results

International audienc