Quality assessment metric of stereo images considering cyclopean integration and visual saliency

Information Visualization Based on Visual Transmission and Multimedia Data Fusion

With the rapid development of information technology, the application media of visual identity design has been greatly broadened, and the requirements of dynamic variability and interactivity of vision have become higher and higher. The introduction of data as an element has brought new semantic endowments to visual design. To solve the problems of noisy image reconstruction and many outliers in traditional art design, this paper uses an improved phase correlation algorithm to reconstruct multimedia video images. In addition, based on the visual characteristics of human eyes, a multi-feature fusion viewpoint image quality evaluation algorithm is proposed. The simulation results show that the method adopted in this paper improves the uneven image texture in art design, and its real effect is greatly enhanced.

Rich Structural Index for Stereoscopic Image Quality Assessment

The human visual system (HVS), affected by viewing distance when perceiving the stereo image information, is of great significance to study of stereoscopic image quality assessment. Many methods of stereoscopic image quality assessment do not have comprehensive consideration for human visual perception characteristics. In accordance with this, we propose a Rich Structural Index (RSI) for Stereoscopic Image objective Quality Assessment (SIQA) method based on multi-scale perception characteristics. To begin with, we put the stereo pair into the image pyramid based on Contrast Sensitivity Function (CSF) to obtain sensitive images of different resolution. Then, we obtain local Luminance and Structural Index (LSI) in a locally adaptive manner on gradient maps which consider the luminance masking and contrast masking. At the same time we use Singular Value Decomposition (SVD) to obtain the Sharpness and Intrinsic Structural Index (SISI) to effectively capture the changes introduced in the image (due to distortion). Meanwhile, considering the disparity edge structures, we use gradient cross-mapping algorithm to obtain Depth Texture Structural Index (DTSI). After that, we apply the standard deviation method for the above results to obtain contrast index of reference and distortion components. Finally, for the loss caused by the randomness of the parameters, we use Support Vector Machine Regression based on Genetic Algorithm (GA-SVR) training to obtain the final quality score. We conducted a comprehensive evaluation with state-of-the-art methods on four open databases. The experimental results show that the proposed method has stable performance and strong competitive advantage.

Estimating Depth-Salient Edges and Its Application to Stereoscopic Image Quality Assessment

The human visual system pays attention to salient regions while perceiving an image. When viewing a stereoscopic 3-D (S3D) image, we hypothesize that while most of the contribution to saliency is provided by the 2-D image, a small but significant contribution is provided by the depth component. Further, we claim that only a subset of image edges contribute to depth perception while viewing an S3D image. In this paper, we propose a systematic approach for depth saliency estimation, called salient edges with respect to depth perception (SED) which localizes the depth-salient edges in an S3D image. We demonstrate the utility of SED in full reference stereoscopic image quality assessment. We consider gradient magnitude and inter-gradient maps for predicting structural similarity. A coarse quality map is estimated first by comparing the 2-D saliency and gradient maps of reference and test stereo pairs. We average this quality map to estimate luminance quality and refine this quality map using SED maps for evaluating depth quality. Finally, we combine this luminance and depth quality to obtain an overall stereo image quality. We perform a comprehensive evaluation of our metric on seven publicly available S3D IQA databases. The proposed metric shows competitive performance on all seven databases with state-of-the-art performance on three of them.

Rectangular-structure-based pose estimation method for non-cooperative rendezvous

Pose estimation for spacecraft is widely recognized as an important technology for space applications. Many space missions require accurate relative pose between the chaser and the target spacecraft. Stereovision is a usual mean to estimate the pose of non-cooperative targets during proximity operations. In this paper, an accurate algorithm for pose estimation is proposed by taking advantage of the geometric structure of the object. With stereo cameras, our approach employs the corners of the solar panel as features and uses bundle adjustment to improve the accuracy. Simulation results demonstrate that our method improves the precision and robustness for non-cooperative spacecraft pose estimation. Our laboratory experiments further validate the approach. The results show that the rotation angle error of our method is within 0.8°, and the measurement error in translation is less than 0.4% at the distance from 3 m to 1.2 m.

Completely blind image quality assessment via image gray-scale fluctuations and fractal dimension analysis

State-of-the-art no-reference image quality assessment methods usually learn to evaluate image quality by regression from the human subjective scores of a training set. Their dependence on the regression algorithm and human subjective scores may limit the practical application of such methods. In this paper, we propose a completely blind image quality assessment method that is highly unsupervised and training free. We first use a specific image primitive to analyze the image gray-scale fluctuation and use this result as one of the image quality assessment features. The box-counting method is then used to evaluate the image fractal dimension, and the result is used as the other feature. Finally, the two features are combined together, and a formula is introduced to calculate a comprehensive image quality feature, which is used to measure the image quality. Experimental results on four open databases show that the newly proposed method correlates well with the human subjective judgments of diversely distorted images.

No-reference stereoscopic image quality assessment guided by visual hierarchical structure and binocular effects

Stereoscopic image quality assessment (SIQA) is an essential technique for modern 3D image and video processing systems serving as performance evaluators and monitors. However, the study on SIQA remains immature due to the complexity of the human visual system (HVS) and binocular effects that binocular vision brings about. To overcome the difficulties, a novel method is proposed that extracts and quantifies image quality-aware features related to cortex areas in charge of visual quality perception, rather than attempting to rigorously simulate the biological processing in HVS, so that the predicting accuracy is preserved while the computational complexity remains moderate. Meanwhile, binocular effects including binocular rivalry and visual discomfort are taken into consideration. Moreover, the proposed method can be operated completely without the assistance of reference images, indicating its wide practical usages. Compared to state-of-the-art works, our method shows evident superiority in terms of effectiveness and robustness.

No-reference quality index for color retinal images

Retinal image quality assessment (RIQA) is essential to assure that the images investigated by ophthalmologists or automatic systems are suitable for reliable medical diagnosis. Measure-based RIQA techniques have several advantages over the more commonly used binary classification-based RIQA methods. Numeric quality measures can aid ophthalmologists in associating a degree of confidence to the diagnosis performed through the investigation of a certain retinal image. Moreover, a numeric quality index can provide a mean for identifying the degree of enhancement required as well as to evaluate and compare the improvement achieved by enhancement techniques. In this work, a no-reference retinal image sharpness numeric quality index is introduced that is computed from the wavelet decomposition of the images. In order to account for the obscured retinal structures in unevenly illuminated image regions, the quality index is modified by a homogeneity parameter calculated from the previously introduced retinal image saturation channel. The proposed quality index was validated and tested on two datasets having different resolutions and quality grades. A strong (Spearman's coefficient > 0.8) and statistically highly significant (p-value < 0.001) correlation was found between the introduced quality index and the subjective human scores for the two different datasets. Moreover, multiclass classification using solely the devised retinal image quality index as a feature resulted in a micro average F-measure of 0.84 and 0.95 using the high and low resolution datasets, respectively. Several comparisons with other retinal image quality measures demonstrated superiority of the proposed quality index in both performance and speed.