Search

Though blind image quality assessment (BIQA) is highly desired in perceptual-oriented image processing systems, it is extremely difficult to design a reliable BIQA method. With the help of the prior knowledge, the human visual system (HVS) hierarchically perceives the quality degradation during the visual recognition. Inspired by this, we suggest different levels of distortion generate individual degradations on hierarchical features, and propose to consider the degradations on both low and high level features for quality prediction. By mimicking the orientation selectivity (OS) mechanism in the primary visual cortex, an OS based local structure is designed for low-level visual information representation. At the meantime, the deep residual network, which possesses multiple levels for feature integration, is employed to extract the deep semantics for high-level visual content representation. By fusing the local structure and the deep semantics, a hierarchical feature set is acquired. Next, the correlations between the degradations of image qualities and their corresponding hierarchical feature sets are analyzed, and a novel hierarchical feature degradation (HFD) based BIQA (HFD-BIQA) method is built. Experimental results on the legacy and wild image quality assessment databases demonstrate the prediction accuracy of the proposed HFD-BIQA method, and verify that the HFD-BIQA performs highly consistent with the subjective perception. [ABSTRACT FROM AUTHOR]

Highlights • A novel blind image quality assessment is proposed. • The proposed method evaluates distorted image from a completely new perspective of human subjective perception. • The experiment results demonstrate the superiority of the proposed method. Abstract No-reference (NR) image quality assessment (IQA) aims to evaluate the quality of an image without reference image, which is greatly desired in the automatic visual signal processing system. Distortions degrade the visual contents and affect the semantics acquisition during the process of human perception. Although the existing methods evaluate the quality of images based on the structure, texture, or statistical characteristics, and deliver high quality prediction accuracy, they do not take the spatial semantics into account. From the perspective of human perception, distortions decrease the structural semantics that represent the structural information, and disturb the spatial semantics that describe the contents of images. Therefore, we attempt to measure the image quality by its degradation of semantics in an image. To extract the semantics of an image, a semantic network is proposed. The network contains convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) that correspond to structural semantics and spatial semantics, respectively. CNN can be regarded as a coarse imitation of human visual mechanism to obtain the structural information, and LSTM can express the contents of an image. Then, by measuring the degradations of different semantics on images, a novel NR IQA is introduced. The proposed approach is evaluated on the databases of LIVE, CSIQ, TID2013, and LIVE multiply distorted database as well as LIVE in the wild image quality challenge database, and the results show superior performance to other state-of-the-art NR IQA methods. Furthermore, we explore the generalization capability of the proposed approach, and the experimental results indicate the proposed approach has a high robustness. [ABSTRACT FROM AUTHOR]

Coding artifacts are annoying in highly compressed signals. Most of the existing artifact reduction methods are designed for one specific type of artifacts, codecs, and bitrates, which are complex and exclusive for one type of artifact reduction. Since both the compressed image/video and the coding error contain information of the original signal, they are highly correlated. Therefore, we try to recover some lost data based on the correlation between the compressed signal and the coding error, and introduce a novel and universal artifact reduction method. Firstly, according to the spatial correlation among pixels, a pixel-adaptive anisotropic filter is designed to reconstruct the distorted signal. Next, a globally optimal filter is designed to further recover the coding loss. Experimental results demonstrate that within an extensive range of bitrates, the proposed method achieves about 0.8 dB, 0.45 dB, 0.3 dB, and 0.2 dB on average of PSNR improvement for JPEG, MPEG4, H.264/AVC, and HEVC compressed signals, respectively. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present a redundancy reduction based approach for computational bottom-up visual saliency estimation. In contrast to conventional methods, our approach determines the saliency by filtering out redundant contents instead of measuring their significance. To analyze the redundancy of self-repeating spatial structures, we propose a non-local self-similarity based procedure. The result redundancy coefficient is used to compensate the Shannon entropy, which is based on statistics of pixel intensities, to generate the bottom-up saliency map of the visual input. Experimental results on three publicly available databases demonstrate that the proposed model is highly consistent with the subjective visual attention. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a novel just noticeable difference (JND) threshold estimation model based on a spatial masking function taking both luminance difference and structural regularity into account. Existing spatial masking functions underestimate the JND threshold for irregular textural regions, because they mainly consider the amplitude of luminance change for simplicity. As regular areas show weak masking effect due to their self-similar structures while irregular regions present strong masking effect, the spatial structure directly determines spatial masking. To effectively measure structural regularity in images under different contents, we propose an adaptive non-local self-similarity analysis based procedure. Then we weight luminance differences with similarity coefficients and deduce a new spatial masking function. Finally, an accurate JND estimation model is introduced. Experimental results demonstrate that the proposed JND model has a better visual effect than other models: it injects much noise into the insensitive regions, whereas little into the sensitive regions. [Copyright &y& Elsevier]

Images are subject to blocking artifacts when they are compressed using the JPEG standard. Knowing the extent of blocking artifacts is thus necessary for such applications as automatic quality monitoring and restoration. The current blocking artifacts measures are based on a strong prior that the block boundaries are known in advance, which often does not hold in real-world applications. Therefore, their performances degrade significantly when block boundaries are misaligned. To address the problem, this paper presents a robust no-reference blocking artifacts evaluation metric for JPEG images based on grid strength and regularity (GridSAR). The underlying idea is to extract block grids from a JPEG image and quantify the grid image to evaluate the strength of blocking artifacts. To this end, a grid map of blocking artifacts is first extracted from the image in the spatial domain. Then the blocking artifacts of the image are evaluated by quantifying the strength and regularity of the grid image. Furthermore, in order to account for the varying sensitivities of human eyes to the blocking artifacts in smooth and textured areas, a masking function is also proposed. Experimental results on seven popular image quality databases demonstrate that GridSAR achieves the state-of-the-art performance, and is robust to block misalignments. [ABSTRACT FROM AUTHOR]

Highlights • We propose a no-reference quality metric for DIBR-synthesized videos. • A two-stage flickering region detection method is proposed. • The experiments have demonstrated the best performance of the proposed method. Depth-image-based-rendering (DIBR) is the most popular view synthesis method. The rendering process in DIBR introduces artifacts to synthesized views. Temporal inconsistency of these artifacts causes flickering, which is one of the dominant distortions in DIBR-synthesized videos. We propose a no-reference quality index for DIBR-synthesized videos. A two-stage flickering region detection method is first proposed by calculating the gradient distance between the matching blocks in adjacent frames followed by a refinement operation. Subsequently, the distortion intensity of flickering regions is measured in singular value decomposition domain. The average flickering intensity is computed as the quality score. Experimental results show that our method outperforms the state-of-the-arts on the IRCCyN/IVC database and ranks the second on the SIAT database, which is only slightly worse than the best-performing one. Furthermore, the presented method is applied to improve the performances of existing metrics and benchmark DIBR algorithms, which achieves very promising results for both tasks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Acupuncture, taking the advantage of modality-specific neural pathways, has shown promising results in the treatment of brain disorders that affect different modalities such as pain and vision. However, the precise underlying mechanisms of within-modality neuromodulation of acupoints on human high-order cognition remain largely unknown. In the present study, we used a non-invasive and easy-operating method, focused ultrasound, to stimulate two language-relevant acupoints, namely GB39 (Xuanzhong) and SJ8 (Sanyangluo), of thirty healthy adults. The effect of focused ultrasound stimulation (FUS) on brain activation was examined by functional magnetic resonance imaging (fMRI). We found that stimulating GB39 and SJ8 by FUS evoked overlapping but distinct brain activation patterns. Our findings provide a major step toward within-modality (in this case, language) acupoint-brain (acubrain) mapping and shed light on to the potential use of FUS as a personalized treatment option for brain disorders that affect high-level cognitive functions. [ABSTRACT FROM AUTHOR]