A fast reliable computational quality predictor is eagerly desired in practical image/video applications, such as serving for the quality monitoring of real-time coding and transcoding. In this ...paper, we propose a new perceptual image quality assessment (IQA) metric based on the human visual system (HVS). The proposed IQA model performs efficiently with convolution operations at multiscales, gradient magnitude, and color information similarity, and a perceptual-based pooling. Extensive experiments are conducted using four popular large-size image databases and two multiply distorted image databases, and results validate the superiority of our approach over modern IQA measures in efficiency and efficacy. Our metric is built on the theoretical support of the HVS with lately designed IQA methods as special cases.
In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global ...feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods.
The human visual system exhibits multiscale characteristic when perceiving visual scenes. The hierarchical structures of an image are contained in its scale space representation, in which the image ...can be portrayed by a series of increasingly smoothed images. Inspired by this, this paper presents a no-reference and robust image sharpness evaluation (RISE) method by learning multiscale features extracted in both the spatial and spectral domains. For an image, the scale space is first built. Then sharpness-aware features are extracted in gradient domain and singular value decomposition domain, respectively. In order to take into account the impact of viewing distance on image quality, the input image is also down-sampled by several times, and the DCT-domain entropies are calculated as quality features. Finally, all features are utilized to learn a support vector regression model for sharpness prediction. Extensive experiments are conducted on four synthetically and two real blurred image databases. The experimental results demonstrate that the proposed RISE metric is superior to the relevant state-of-the-art methods for evaluating both synthetic and real blurring. Furthermore, the proposed metric is robust, which means that it has very good generalization ability.
In a tampered blurred image generated by splicing, the spliced region and the original image may have different blur types. Splicing localization in this image is a challenging problem when a forger ...uses some postprocessing operations as antiforensics to remove the splicing traces anomalies by resizing the tampered image or blurring the spliced region boundary. Such operations remove the artifacts that make detection of splicing difficult. In this paper, we overcome this problem by proposing a novel framework for blurred image splicing localization based on the partial blur type inconsistency. In this framework, after the block-based image partitioning, a local blur type detection feature is extracted from the estimated local blur kernels. The image blocks are classified into out-of-focus or motion blur based on this feature to generate invariant blur type regions. Finally, a fine splicing localization is applied to increase the precision of regions boundary. We can use the blur type differences of the regions to trace the inconsistency for the splicing localization. Our experimental results show the efficiency of the proposed method in the detection and the classification of the out-of-focus and motion blur types. For splicing localization, the result demonstrates that our method works well in detecting the inconsistency in the partial blur types of the tampered images. However, our method can be applied to blurred images only.
Blur is a key determinant in the perception of image quality. Generally, blur causes spread of edges, which leads to shape changes in images. Discrete orthogonal moments have been widely studied as ...effective shape descriptors. Intuitively, blur can be represented using discrete moments since noticeable blur affects the magnitudes of moments of an image. With this consideration, this paper presents a blind image blur evaluation algorithm based on discrete Tchebichef moments. The gradient of a blurred image is first computed to account for the shape, which is more effective for blur representation. Then the gradient image is divided into equal-size blocks and the Tchebichef moments are calculated to characterize image shape. The energy of a block is computed as the sum of squared non-DC moment values. Finally, the proposed image blur score is defined as the variance-normalized moment energy, which is computed with the guidance of a visual saliency model to adapt to the characteristic of human visual system. The performance of the proposed method is evaluated on four public image quality databases. The experimental results demonstrate that our method can produce blur scores highly consistent with subjective evaluations. It also outperforms the state-of-the-art image blur metrics and several general-purpose no-reference quality metrics.
Recent advances in sparse representation show that overcomplete dictionaries learned from natural images can capture high-level features for image analysis. Since atoms in the dictionaries are ...typically edge patterns and image blur is characterized by the spread of edges, an overcomplete dictionary can be used to measure the extent of blur. Motivated by this, this paper presents a no-reference sparse representation-based image sharpness index. An overcomplete dictionary is first learned using natural images. The blurred image is then represented using the dictionary in a block manner, and block energy is computed using the sparse coefficients. The sharpness score is defined as the variance-normalized energy over a set of selected high-variance blocks, which is achieved by normalizing the total block energy using the sum of block variances. The proposed method is not sensitive to training images, so a universal dictionary can be used to evaluate the sharpness of images. Experiments on six public image quality databases demonstrate the advantages of the proposed method.
View synthesis is a key technique in free-viewpoint video, which renders virtual views based on texture and depth images. The distortions in synthesized views come from two stages, i.e., the stage of ...the acquisition and processing of texture and depth images, and the rendering stage using depth-image-based-rendering (DIBR) algorithms. The existing view synthesis quality metrics are designed for the distortions caused by a single stage, which cannot accurately evaluate the quality of the entire view synthesis process. With the considerations that the distortions introduced by two stages both cause edge degradation and texture unnaturalness, and the Difference-of-Gaussian (DoG) representation is powerful in capturing image edge and texture characteristics by simulating the center-surrounding receptive fields of retinal ganglion cells of human eyes, this paper presents a no-reference quality index for Synthesized views using DoG-based Edge statistics and Texture naturalness (SET). To mimic the multi-scale property of the human visual system (HVS), DoG images are first calculated at multiple scales. Then, the orientation selective statistics features and the texture naturalness features are calculated on the DoG images and the coarsest scale image, producing two groups of quality-aware features. Finally, the quality model is learnt from these features using the random forest regression model. The experimental results on two view synthesis image databases demonstrate that the proposed metric is advantageous over the relevant state of the art in dealing with the distortions in the whole view synthesis process.
During the past few years, there have been various kinds of content-aware image retargeting operators proposed for image resizing. However, the lack of effective objective retargeting quality ...assessment metrics limits the further development of image retargeting techniques. Different from traditional image quality assessment (IQA) metrics, the quality degradation during image retargeting is caused by artificial retargeting modifications, and the difficulty for image retargeting quality assessment (IRQA) lies in the alternation of the image resolution and content, which makes it impossible to directly evaluate the quality degradation like traditional IQA. In this paper, we interpret the image retargeting in a unified framework of resampling grid generation and forward resampling. We show that the geometric change estimation is an efficient way to clarify the relationship between the images. We formulate the geometric change estimation as a backward registration problem with Markov random field and provide an effective solution. The geometric change aims to provide the evidence about how the original image is resized into the target image. Under the guidance of the geometric change, we develop a novel aspect ratio similarity (ARS) metric to evaluate the visual quality of retargeted images by exploiting the local block changes with a visual importance pooling strategy. Experimental results on the publicly available MIT RetargetMe and CUHK data sets demonstrate that the proposed ARS can predict more accurate visual quality of retargeted images compared with the state-of-the-art IRQA metrics.
Medical images are used as an important basis for diagnosing diseases, among which CT images are seen as an important tool for diagnosing lung lesions. However, manual segmentation of infected areas ...in CT images is time-consuming and laborious. With its excellent feature extraction capabilities, a deep learning-based method has been widely used for automatic lesion segmentation of COVID-19 CT images. However, the segmentation accuracy of these methods is still limited. To effectively quantify the severity of lung infections, we propose a Sobel operator combined with multi-attention networks for COVID-19 lesion segmentation (SMA-Net). In our SMA-Net method, an edge feature fusion module uses the Sobel operator to add edge detail information to the input image. To guide the network to focus on key regions, SMA-Net introduces a self-attentive channel attention mechanism and a spatial linear attention mechanism. In addition, the Tversky loss function is adopted for the segmentation network for small lesions. Comparative experiments on COVID-19 public datasets show that the average Dice similarity coefficient (DSC) and joint intersection over union (IOU) of the proposed SMA-Net model are 86.1% and 77.8%, respectively, which are better than those in most existing segmentation networks.
This letter presents a Referenceless quality Measure of Blocking artifacts (RMB) using Tchebichef moments. It is based on the observation that Tchebichef kernels with different orders have varying ...abilities to capture blockiness. In a block manner, high-odd-order moments are computed to score the blocking artifacts. The blockiness scores are further weighted to incorporate the characteristic of Human Visual System (HVS), which is achieved by classifying the blocks into smooth and textured. Experimental results and comparisons demonstrate the advantage of the proposed method.
PDF
