Jiansheng Qian

Color image quality assessment based on sparse representation and reconstruction residual

Color sparse representation is used to capture structure and color distortions in a holistic manner.Reconstruction residual is used to capture contrast changes.Sparse features are also used to conduct the pooling.The proposed method is advantageous over the state-of-the-arts on both traditional and color distortions. Image quality assessment (IQA) is a fundamental problem in image processing. While in practice almost all images are represented in the color format, most of the current IQA metrics are designed in gray-scale domain. Color influences the perception of image quality, especially in the case where images are subject to color distortions. With this consideration, this paper presents a novel color image quality index based on Sparse Representation and Reconstruction Residual (SRRR). An overcomplete color dictionary is first trained using natural color images. Then both reference and distorted images are represented using the color dictionary, based on which two feature maps are constructed to measure structure and color distortions in a holistic manner. With the consideration that the feature maps are insensitive to image contrast change, the reconstruction residuals are computed and used as a complementary feature. Additionally, luminance similarity is also incorporated to produce the overall quality score for color images. Experiments on public databases demonstrate that the proposed method achieves promising performance in evaluating traditional distortions, and it outperforms the existing metrics when used for quality evaluation of color-distorted images.

Multimodal medical image fusion based on discrete Tchebichef moments and pulse coupled neural network

Multimodal medical image fusion plays a vital role in clinical diagnoses and treatment planning. In many image fusion methods‐based pulse coupled neural network (PCNN), normalized coefficients are used to motivate the PCNN, and this makes the fused image blur, detail loss, and decreases contrast. Moreover, they are limited in dealing with medical images with different modalities. In this article, we present a new multimodal medical image fusion method based on discrete Tchebichef moments and pulse coupled neural network to overcome the aforementioned problems. First, medical images are divided into equal‐size blocks and the Tchebichef moments are calculated to characterize image shape, and energy of blocks is computed as the sum of squared non‐DC moment values. Then to retain edges and textures, the energy of Tchebichef moments for blocks is introduced to motivate the PCNN with adaptive linking strength. Finally, large firing times are selected as coefficients of the fused image. Experimental results show that the proposed scheme outperforms state‐of‐the‐art methods and it is more effective in processing medical images with different modalities.

Perceptual quality evaluation for image defocus deblurring

Abstract Blur is one of the most common distortion types in image acquisition. Image deblurring has been widely studied as an effective technique to improve the quality of blurred images. However, little work has been done to the perceptual evaluation of image deblurring algorithms and deblurred images. In this paper, we conduct both subjective and objective studies of image defocus deblurring. A defocus deblurred image database (DDID) is first built using state-of-the-art image defocus deblurring algorithms, and subjective test is carried out to collect the human ratings of the images. Then the performances of the deblurring algorithms are evaluated based on the subjective scores. With the observation that the existing image quality metrics are limited in predicting the quality of defocus deblurred images, a quality enhancement module is proposed based on Gray Level Co-occurrence Matrix (GLCM), which is mainly used to measure the loss of texture naturalness caused by deblurring. Experimental results based on the DDID database demonstrate the effectiveness of the proposed method.

An efficient and effective blind camera image quality metric via modeling quaternion wavelet coefficients

Abstract As an extension of Discrete and Complex Wavelet Transform, Quaternion Wavelet Transform (QWT) has attracted extensive attention in the past few years, because it can provide better analytic representation for 2D images. The QWT of an image consists of four parts, i.e., one magnitude part and three phase parts. The magnitude is nearly shift-invariant, which characterizes features at any spatial location, and the three phases represent the structure of these features. This indicates that QWT is more powerful in representing image structures, and thus is suitable for image quality evaluation. In this paper, an efficient and effective Camera Image Quality Metric (CIQM) is proposed based on QWT, which is utilized to describe the intrinsic structures of an image. For an image, it is first decomposed by QWT with three scales. Then, for each scale, the magnitude and entropy of the subband coefficients, and natural scene statistics of the third phase are calculated. The magnitude is utilized to describe the generalized spectral behavior, and the entropy is used to encode the generalized information of distortions. Since the third phase of QWT is considered to be texture feature, the natural scene statistics of the third phase of QWT is used to measure structure degradations in the proposed method. All these features reflect the self-similarity and independency of image content, which can effectively reflect image distortions. Finally, random forest is utilized to build the quality model. Experiments conducted on three camera image databases and two multiply distorted image databases have proved that CIQM outperforms the relevant state-of-the-art models for both authentically distorted images and multiply distorted images.

Training-free referenceless camera image blur assessment via hypercomplex singular value decomposition

Blur plays an important role in the perception of camera image quality. Generally, blur leads to attenuation of high frequency information and accordingly changes the image energy. Quaternion describes the color information as a whole. Recent researches in quaternion singular value decomposition show that the singular values and singular vectors of the quaternion can capture the distortion of color images, and thus we reasonably suppose that singular values can be utilized to evaluate the sharpness of camera images. Motivated by this, a novel training-free blind quality assessment method considering the integral color information and singular values of the distorted image is proposed to evaluate the sharpness of camera images. The blurred camera image is first converted to LAB color space and divided into blocks. Then pure quaternion is utilized to represent pixels of the blurred camera image and the energy of every block are obtained. Inspired by the human visual system appears to assess image sharpness based on the sharpest region of the image, the local sharpness normalized energy is defined as the sharpness score of the blurred camera image. Experimental results have demonstrated the effectiveness of the proposed metric compared with popular sharpness image quality metrics.

Perceptual evaluation of Compressive Sensing Image Recovery

Compressive sensing (CS) has been attracting tremendous attention in recent years. Extensive CS recovery algorithms have been proposed for effective image reconstruction. However, little work has been dedicated to the perceptual evaluation of CS image recovery algorithms and the corresponding recovered images. In this paper, we first build a Compressive Sensing Recovered Image Database (CSRID), which contains images generated by ten popular CS image recovery algorithms at different sensing rates. We then carry out a subjective experiment using the single-stimulus method to obtain the subjective qualities of the images. The subjective scores are then used to evaluate the performances of the CS image recovery algorithms. Finally, the performances of general-purpose no-reference (NR) quality metrics and image blur metrics are investigated on the CSRID database. Experimental results show that the state-of-the-art quality metrics are very limited in predicting the quality of CS recovered images.

No-Reference Quality Assessment of Camera-Captured Distortion Images

In this paper we address the problem of quality assessment of camera images using three types of features. The first type of features measures the naturalness of an image, inspired by a recent finding that there exists high correlation between structural degradation and free energy entropy on natural scene images and this regulation will be gradually devastated as more distortions are introduced. The second type of features comes from an observation that a broad spectrum of statistics of distorted images can be caught by the generalized Gaussian distribution (GGD) according to natural scene statistics (NSS). These two groups of features are both based on NSS regulations, but they come from the considerations of local auto-regression and global histogram, respectively. The third type of features estimates the local sharpness by computing log-energies in the discrete wavelet transform domain. Finally our quality metric is achieved via an SVR-based machine learning tool and its performance is proved to be statistically better than state-of-the-art competitors on the CID2013 database, which is dedicated to the quality assessment of camera-captured images.

No reference quality evaluation of medical image fusion

Medical image fusion (MIF) attracts much attention in clinical use. Many MIF algorithms have been proposed over the past decade. Existing MIF algorithms create different fused image, however, current quality evaluation method is not designed for MIF images. So, we present a no reference quality evaluation of medical image fusion. Firstly, a MIF image database (MIFID) is built, and radiologist ratings are selected to conduct subjective test. Then an objective quality evaluation metric of medical image fusion is proposed via the phase congruency and standard deviation. Image salient features and image information are very important for visual quality of fused image. Based on this consideration, we combine the two existing quality evaluation metrics to assess MIF images. Finally, five comparative study experiments are implemented based on the MIFID. Experimental results reveal that the proposed quality evaluation metric is superior to the existing state‐of‐the‐art metrics, which is more applicable to evaluate MIF images.

Perceptual quality evaluation for motion deblurring

Motion deblurring has been widely studied. However, the relevant quality evaluation of motion deblurred images remains an open problem. The motion deblurred images are usually contaminated by noise, ringing and residual blur (NRRB) simultaneously. Unfortunately, most of the existing quality metrics are not designed for multiply distorted images, so they are limited in predicting the quality of motion deblurred images. In this study, the authors propose a new quality metric for motion deblurred images by measuring NRRB. For a motion deblurred image, the noise level is first estimated. Then the ringing effect is measured by incorporating visual saliency model to adapt to the characteristic of the human visual system. A reblurring-based method is proposed to extract similarity features between a motion deblurred image and its re-blurred version for evaluating the residual blur. Finally, the overall quality score of a motion deblurred image is obtained by pooling the scores of noise, ringing and blur. Experimental results conducted on a motion deblurring database demonstrate that the proposed metric significantly outperforms the existing quality metrics. In addition, the proposed NRRB metric is used for improving the existing general-purpose no-reference metrics, and very encouraging results are achieved.

No-reference quality assessment of compressive sensing image recovery

Abstract Compressive sensing (CS) has been attracting tremendous attention in recent years. Extensive CS image recovery algorithms have been proposed for more effective image reconstruction. However, very little work has been addressed for the quality assessment of CS recovered images, which may hinder further development of CS image recovery techniques. The CS recovered images are typically contaminated by multiple distortions, particularly at low sampling rates. Unfortunately, the existing quality metrics are ineffective for evaluating multiple distortions, so they are limited in predicting the quality of CS recovered images. Motivated by this, this paper presents a no-reference CS Recovered Image Quality (CSRIQ) metric based on the measurement of both local and global distortions in CS recovered images. The local features consist of a local phase coherence based edge sharpness measure and a gray level co-occurrence matrix based texture measure. The global features are extracted based on the natural scene statistics and can be divided into two types. One type is obtained by calculating image naturalness parameters. The other type is computed based on the statistics of singular value decomposition coefficients. Support vector regression is employed to do model training and the subsequent quality prediction. Experimental results conducted on a CS recovered image database demonstrate the advantages of the proposed method. As a application, the proposed metric is used for automatic parameter selection for CS image recovery algorithms.