A Randomized Comparison Between Anterior Talofibular Ligament Repair Using Broström Operation with and without an Internal Brace.

The aim of the study was to compare the intermediate-term (>24 months) clinical outcomes between anterior talofibular ligament repair using Broström operation with and without an internal brace. Nineteen patients underwent surgery using an arthroscopic traditional Broström repair with an internal brace technique (IB) and Eighteen patients underwent surgery using an arthroscopic traditional Broström repair without an internal brace technique (TB) . All patients were evaluated clinically using the Foot and Ankle Outcome Score (FAOS) and Foot and Ankle Ability Measure (FAAM). According to FAAM, sports activity scores of TB and IB groups were 83.33 ± 5.66 and 90.63 ± 6.21 at the final follow-up (p = .02). There were no significant differences in preoperative and postoperative stress radiographs between the two groups. Total medical expense was more in the IB group (p < .001). It also has a significant superiority in the terms of FAAM scores at sports activity. However, there was no difference during daily life.

Client-Oriented Blind Quality Metric for High Dynamic Range Stereoscopic Omnidirectional Vision Systems.

A high dynamic range (HDR) stereoscopic omnidirectional vision system can provide users with more realistic binocular and immersive perception, where the HDR stereoscopic omnidirectional image (HSOI) suffers distortions during its encoding and visualization, making its quality evaluation more challenging. To solve the problem, this paper proposes a client-oriented blind HSOI quality metric based on visual perception. The proposed metric mainly consists of a monocular perception module (MPM) and binocular perception module (BPM), which combine monocular/binocular, omnidirectional and HDR/tone-mapping perception. The MPM extracts features from three aspects: global color distortion, symmetric/asymmetric distortion and scene distortion. In the BPM, the binocular fusion map and binocular difference map are generated by joint image filtering. Then, brightness segmentation is performed on the binocular fusion image, and distinctive features are extracted on the segmented high/low/middle brightness regions. For the binocular difference map, natural scene statistical features are extracted by multi-coefficient derivative maps. Finally, feature screening is used to remove the redundancy between the extracted features. Experimental results on the HSOID database show that the proposed metric is generally better than the representative quality metric, and is more consistent with the subjective perception.

DNet: Dynamic Neighborhood Feature Learning in Point Cloud.

Neighborhood selection is very important for local region feature learning in point cloud learning networks. Different neighborhood selection schemes may lead to quite different results for point cloud processing tasks. The existing point cloud learning networks mainly adopt the approach of customizing the neighborhood, without considering whether the selected neighborhood is reasonable or not. To solve this problem, this paper proposes a new point cloud learning network, denoted as Dynamic neighborhood Network (DNet), to dynamically select the neighborhood and learn the features of each point. The proposed DNet has a multi-head structure which has two important modules: the Feature Enhancement Layer (FELayer) and the masking mechanism. The FELayer enhances the manifold features of the point cloud, while the masking mechanism is used to remove the neighborhood points with low contribution. The DNet can learn the manifold features and spatial geometric features of point cloud, and obtain the relationship between each point and its effective neighborhood points through the masking mechanism, so that the dynamic neighborhood features of each point can be obtained. Experimental results on three public datasets demonstrate that compared with the state-of-the-art learning networks, the proposed DNet shows better superiority and competitiveness in point cloud processing task.

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis.

Multiview video plus depth is one of the mainstream representations of 3D scenes in emerging free viewpoint video, which generates virtual 3D synthesized images through a depth-image-based-rendering (DIBR) technique. However, the inaccuracy of depth maps and imperfect DIBR techniques result in different geometric distortions that seriously deteriorate the users' visual perception. An effective 3D synthesized image quality assessment (IQA) metric can simulate human visual perception and determine the application feasibility of the synthesized content. In this paper, a no-reference IQA metric based on visual-entropy-guided multi-layer features analysis for 3D synthesized images is proposed. According to the energy entropy, the geometric distortions are divided into two visual attention layers, namely, bottom-up layer and top-down layer. The feature of salient distortion is measured by regional proportion plus transition threshold on a bottom-up layer. In parallel, the key distribution regions of insignificant geometric distortion are extracted by a relative total variation model, and the features of these distortions are measured by the interaction of decentralized attention and concentrated attention on top-down layers. By integrating the features of both bottom-up and top-down layers, a more visually perceptive quality evaluation model is built. Experimental results show that the proposed method is superior to the state-of-the-art in assessing the quality of 3D synthesized images.

Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features.

With the wide applications of three-dimensional (3D) meshes in intelligent manufacturing, digital animation, virtual reality, digital cities and other fields, more and more processing techniques are being developed for 3D meshes, including watermarking, compression, and simplification, which will inevitably lead to various distortions. Therefore, how to evaluate the visual quality of 3D mesh is becoming an important problem and it is necessary to design effective tools for blind 3D mesh quality assessment. In this paper, we propose a new Blind Mesh Quality Assessment method based on Graph Spectral Entropy and Spatial features, called as BMQA-GSES. 3D mesh can be represented as graph signal, in the graph spectral domain, the Gaussian curvature signal of the 3D mesh is firstly converted with Graph Fourier transform (GFT), and then the smoothness and information entropy of amplitude features are extracted to evaluate the distortion. In the spatial domain, four well-performing spatial features are combined to describe the concave and convex information and structural information of 3D meshes. All the extracted features are fused by the random forest regression to predict the objective quality score of the 3D mesh. Experiments are performed successfully on the public databases and the obtained results show that the proposed BMQA-GSES method provides good correlation with human visual perception and competitive scores compared to state-of-art quality assessment methods.

No-reference high-dynamic-range image quality assessment based on tensor decomposition and manifold learning.

The practical applications of the full-reference image quality assessment (IQA) method are limited. Here, we propose a new no-reference quality assessment method for high-dynamic-range (HDR) images. First, tensor decomposition is used to generate three feature maps of an HDR image, considering color and structure information of the HDR image. Second, for a given HDR image, because its first feature map contains its main energy and important structural feature information, manifold learning is used in the first feature map to find the inherent geometric structure of high-dimensional data in a low-dimensional manifold. In addition, the corresponding multi-scale manifold structure features are extracted from the first feature map. For the second and third feature maps of the HDR image, multi-scale contrast features are extracted, as they reflect the perceived detail contrast information of the HDR image. Finally, the extracted features are aggregated by support vector regression to obtain the objective quality prediction score of the HDR image. Experimental results show that the proposed method is superior to some representative full- and no-reference methods, and even superior to the full-reference HDR IQA method, HDR-VDP-2.2, on the Nantes database. The proposed method has a higher consistency with human visual perception.

Optimizing multiview video plus depth retargeting technique for stereoscopic 3D displays.

In a multiview video plus depth (MVD) based three-dimensional (3D) video system, the generation of the contents with simultaneous resolution and depth adjustments is very challenging. In this paper, we have presented a Multiview Video plus Depth ReTargeting (MVDRT) technique for stereoscopic 3D (S3D) displays. The main motivation of this work is to optimize the resolution and depth of original MVD data so that it is suitable for view synthesis. Our method takes shape preservation, line bending and visual comfort constraints into account, and simultaneously optimizes the horizontal, vertical and depth coordinates in display space. The retargeted MVD data is used to generate the contents for S3D displays. Experimental results demonstrate our method can achieve a better view synthesis performance than other approaches that still preserve the original depth information after retargeting, leading to promising S3D experience.

Virtual-view PSNR prediction based on a depth distortion tolerance model and support vector machine.

Quality prediction of virtual-views is important for free viewpoint video systems, and can be used as feedback to improve the performance of depth video coding and virtual-view rendering. In this paper, an efficient virtual-view peak signal to noise ratio (PSNR) prediction method is proposed. First, the effect of depth distortion on virtual-view quality is analyzed in detail, and a depth distortion tolerance (DDT) model that determines the DDT range is presented. Next, the DDT model is used to predict the virtual-view quality. Finally, a support vector machine (SVM) is utilized to train and obtain the virtual-view quality prediction model. Experimental results show that the Spearman's rank correlation coefficient and root mean square error between the actual PSNR and the predicted PSNR by DDT model are 0.8750 and 0.6137 on average, and by the SVM prediction model are 0.9109 and 0.5831. The computational complexity of the SVM method is lower than the DDT model and the state-of-the-art methods.

Optimizing visual comfort for stereoscopic 3D display based on color-plus-depth signals.

Visual comfort is a long-facing problem in stereoscopic 3D (S3D) display. In this paper, targeting to produce S3D content based on color-plus-depth signals, a general framework for depth mapping to optimize visual comfort for S3D display is proposed. The main motivation of this work is to remap the depth range of color-plus-depth signals to a new depth range that is suitable to comfortable S3D display. Towards this end, we first remap the depth range globally based on the adjusted zero disparity plane, and then present a two-stage global and local depth optimization solution to solve the visual comfort problem. The remapped depth map is used to generate the S3D output. We demonstrate the power of our approach on perceptually uncomfortable and comfortable stereoscopic images.

Simulating receptive fields of human visual cortex for 3D image quality prediction.

Quality assessment of 3D images presents many challenges when attempting to gain better understanding of the human visual system. In this paper, we propose a new 3D image quality prediction approach by simulating receptive fields (RFs) of human visual cortex. To be more specific, we extract the RFs from a complete visual pathway, and calculate their similarity indices between the reference and distorted 3D images. The final quality score is obtained by determining their connections via support vector regression. Experimental results on three 3D image quality assessment databases demonstrate that in comparison with the most relevant existing methods, the devised algorithm achieves high consistency alignment with subjective assessment, especially for asymmetrically distorted stereoscopic images.

Naturalness index for a tone-mapped high dynamic range image.

High dynamic range (HDR) images can only be backward-compatible with existing low dynamic range (LDR) imaging systems after being processed by tone-mapping operators. Hence, the quality assessment (QA) of tone-mapped HDR images has become an important and challenging issue in HDR imaging research. In this paper, we propose a naturalness index for a tone-mapped image to predict its quality. First, we extract the statistical features of the tone-mapped image's luminance value and use it to evaluate the brightness naturalness with no reference information. Meanwhile, we use perceptive color, image contrast, and detail information to represent the image content and predict their naturalness qualities, respectively. Then, the four components of the naturalness qualities are combined to yield the overall naturalness quality of the tone-mapped image. Experimental results on a publicly available database demonstrated that, in comparison with a traditional LDR image QA method and a leading tone-mapped image QA method, the proposed method has better performance in evaluating a tone-mapped image's quality.

Monocular-binocular feature fidelity induced index for stereoscopic image quality assessment.

Perceptual three-dimensional image quality assessment (3D-IQA) aims to use computational models to measure image quality in a way that is consistent with human visual perception. Unlike previous studies that directly extended the two-dimensional metrics to the three-dimensional (3D) case, the major technical innovation of this paper is to simulate monocular and binocular visual perception and propose a monocular-binocular feature fidelity induced index for 3D-IQA. To be more specific, in the training stage, we train monocular and binocular dictionaries from the training database, so that the latent response properties can be represented as a set of basis vectors. In the quality estimation stage, we compute monocular feature fidelity and binocular feature fidelity indexes based on the estimated sparse coefficient vectors and compute a global energy response similarity index by considering global energy changes. The final quality score is obtained by incorporating them. Experimental results on four 3D-IQA databases demonstrate that in comparison with the most relevant existing methods, the devised algorithm achieves high consistency alignment with subjective assessment.

A simple quality assessment index for stereoscopic images based on 3D gradient magnitude.

We present a simple quality assessment index for stereoscopic images based on 3D gradient magnitude. To be more specific, we construct 3D volume from the stereoscopic images across different disparity spaces and calculate pointwise 3D gradient magnitude similarity (3D-GMS) along three horizontal, vertical, and viewpoint directions. Then, the quality score is obtained by averaging the 3D-GMS scores of all points in the 3D volume. Experimental results on four publicly available 3D image quality assessment databases demonstrate that, in comparison with the most related existing methods, the devised algorithm achieves high consistency alignment with subjective assessment.

A novel macroblock level rate control method for stereo video coding.

To compress stereo video effectively, this paper proposes a novel macroblock (MB) level rate control method based on binocular perception. A binocular just-notification difference (BJND) model based on the parallax matching is first used to describe binocular perception. Then, the proposed rate control method is performed in stereo video coding with four levels, namely, view level, group-of-pictures (GOP) level, frame level, and MB level. In the view level, different proportions of bitrates are allocated for the left and right views of stereo video according to the prestatistical rate allocation proportion. In the GOP level, the total number of bitrates allocated to each GOP is computed and the initial quantization parameter of each GOP is set. In the frame level, the target bits allocated to each frame are computed. In the MB level, visual perception factor, which is measured by the BJND value of MB, is used to adjust the MB level bit allocation, so that the rate control results in line with the human visual characteristics. Experimental results show that the proposed method can control the bitrate more accurately and get better subjective quality of stereo video, compared with other methods.

Multi-Attention Learning and Exposure Guidance Toward Ghost-Free High Dynamic Range Light Field Imaging.

Due to sensor limitations, the light field (LF) images captured by the LF camera suffer from low dynamic range and are prone to poor exposure. To solve this problem, combining multi-exposure technology with LF camera imaging can achieve high dynamic range (HDR) LF imaging. However, for dynamic scenes, this approach tends to produce disturbing ghosting artifacts and destroy the parallax structure of the generated results. To this end, this paper proposes a novel ghost-free HDR LF imaging method using multi-attention learning and exposure guidance. Specifically, the proposed method first designs a multi-scale cross-attention module to achieve efficient multi-exposure LF feature alignment. After that, a dual self-attention-driven Transformer block is constructed to excavate the geometric information of LF and fuse the aligned LF features. In particular, exposure masks derived from middle-exposure are introduced in the feature fusion to guide the network to focus on information recovery in low- and high-brightness regions. Besides, a local compensation module is integrated to cope with local alignment errors and refine details. Finally, a multi-objective reconstruction strategy combined with exposure masks is employed to restore high-quality HDR LF images. Extensive experimental results on the benchmark dataset show that the proposed method generates HDR LF results with high spatial-angular quality consistency and outperforms the state-of-the-art methods in quantitative and qualitative comparisons. Furthermore, the proposed method can enhance the performance of existing LF applications, such as depth estimation.

Deep Light Field Spatial Super-Resolution Using Heterogeneous Imaging.

Light field (LF) imaging expands traditional imaging techniques by simultaneously capturing the intensity and direction information of light rays, and promotes many visual applications. However, owing to the inherent trade-off between the spatial and angular dimensions, LF images acquired by LF cameras usually suffer from low spatial resolution. Many current approaches increase the spatial resolution by exploring the four-dimensional (4D) structure of the LF images, but they have difficulties in recovering fine textures at a large upscaling factor. To address this challenge, this paper proposes a new deep learning-based LF spatial super-resolution method using heterogeneous imaging (LFSSR-HI). The designed heterogeneous imaging system uses an extra high-resolution (HR) traditional camera to capture the abundant spatial information in addition to the LF camera imaging, where the auxiliary information from the HR camera is utilized to super-resolve the LF image. Specifically, an LF feature alignment module is constructed to learn the correspondence between the 4D LF image and the 2D HR image to realize information alignment. Subsequently, a multi-level spatial-angular feature enhancement module is designed to gradually embed the aligned HR information into the rough LF features. Finally, the enhanced LF features are reconstructed into a super-resolved LF image using a simple feature decoder. To improve the flexibility of the proposed method, a pyramid reconstruction strategy is leveraged to generate multi-scale super-resolution results in one forward inference. The experimental results show that the proposed LFSSR-HI method achieves significant advantages over the state-of-the-art methods in both qualitative and quantitative comparisons. Furthermore, the proposed method preserves more accurate angular consistency.

Cubemap-Based Perception-Driven Blind Quality Assessment for 360-degree Images.

image can be represented with different formats, such as the equirectangular projection (ERP) image, viewport images or spherical image, for its different processing procedures and applications. Accordingly, the 360-degree image quality assessment (360-IQA) can be performed on these different formats. However, the performance of 360-IQA with the ERP image is not equivalent with those with the viewport images or spherical image due to the over-sampling and the resulted obvious geometric distortion of ERP image. This imbalance problem brings challenge to ERP image based applications, such as 360-degree image/video compression and assessment. In this paper, we propose a new blind 360-IQA framework to handle this imbalance problem. In the proposed framework, cubemap projection (CMP) with six inter-related faces is used to realize the omnidirectional viewing of 360-degree image. A multi-distortions visual attention quality dataset for 360-degree images is firstly established as the benchmark to analyze the performance of objective 360-IQA methods. Then, the perception-driven blind 360-IQA framework is proposed based on six cubemap faces of CMP for 360-degree image, in which human attention behavior is taken into account to improve the effectiveness of the proposed framework. The cubemap quality feature subset of CMP image is first obtained, and additionally, attention feature matrices and subsets are also calculated to describe the human visual behavior. Experimental results show that the proposed framework achieves superior performances compared with state-of-the-art IQA methods, and the cross dataset validation also verifies the effectiveness of the proposed framework. In addition, the proposed framework can also be combined with new quality feature extraction method to further improve the performance of 360-IQA. All of these demonstrate that the proposed framework is effective in 360-IQA and has a good potential for future applications.

Online Learning-Based Multi-Stage Complexity Control for Live Video Coding.

High Efficiency Video Coding (HEVC) can significantly improve the compression efficiency in comparison with the preceding H.264/Advanced Video Coding (AVC) but at the cost of extremely high computational complexity. Hence, it is challenging to realize live video applications on low-delay and power-constrained devices, such as the smart mobile devices. In this article, we propose an online learning-based multi-stage complexity control method for live video coding. The proposed method consists of three stages: multi-accuracy Coding Unit (CU) decision, multi-stage complexity allocation, and Coding Tree Unit (CTU) level complexity control. Consequently, the encoding complexity can be accurately controlled to correspond with the computing capability of the video-capable device by replacing the traditional brute-force search with the proposed algorithm, which properly determines the optimal CU size. Specifically, the multi-accuracy CU decision model is obtained by an online learning approach to accommodate the different characteristics of input videos. In addition, multi-stage complexity allocation is implemented to reasonably allocate the complexity budgets to each coding level. In order to achieve a good trade-off between complexity control and rate distortion (RD) performance, the CTU-level complexity control is proposed to select the optimal accuracy of the CU decision model. The experimental results show that the proposed algorithm can accurately control the coding complexity from 100% to 40%. Furthermore, the proposed algorithm outperforms the state-of-the-art algorithms in terms of both accuracy of complexity control and RD performance.

Unified No-Reference Quality Assessment of Singly and Multiply Distorted Stereoscopic Images.

A challenging problem in the no-reference quality assessment of multiply distorted stereoscopic images (MDSIs) is to simulate the monocular and binocular visual properties under a mixed type of distortions. Due to the joint effects of multiple distortions in MDSIs, the underlying monocular and binocular visual mechanisms have different manifestations with those of singly distorted stereoscopic images (SDSIs). This paper presents a unified no-reference quality evaluator for SDSIs and MDSIs by learning monocular and binocular local visual primitives (MB-LVPs). The main idea is to learn MB-LVPs to characterize the local receptive field properties of the visual cortex in response to SDSIs and MDSIs. Furthermore, we also consider that the learning of primitives should be performed in a task-driven manner. For this, two penalty terms including reconstruction error and quality inconsistency are jointly minimized within a supervised dictionary learning framework, generating a set of quality-oriented MB-LVPs for each single and multiple distortion modality. Given an input stereoscopic image, feature encoding is performed using the learned MB-LVPs as codebooks, resulting in the corresponding monocular and binocular responses. Finally, responses across all the modalities are fused with probabilistic weights which are determined by the modality-specific sparse reconstruction errors, yielding the final monocular and binocular features for quality regression. The superiority of our method has been verified on several SDSI and MDSI databases.

[Treating intractable lateral epicondylitis by extracurricular arthroscopic operation based on pressure point].

OBJECTIVE: To evaluate clinical efficaly of intractable lateral epicondylitis by extracurricular arthroscopic operation based on pressure point. METHODS: From October 2015 to September 2017, 19 patients with intractable lateral epicondylitis were treated with extraarticylar arthroscopic operation based in pressure point. Among patients, including 7 males and 12 females, aged from 33 to 62 years old with an average of(43.16±8.12) years old, The courses of conservative treatment ranged from 7 to 41 months, with an average of(15.47±7.08) months. Postoperative complications were observed, VAS score and Mayo score before and after operation at 3 months were observed and compared. RESULTS: All patients were followed up from 6 to 26 months, with an average (17.16±5.25) months. No infection, skin necrosis and nerve injury occurred. No group weakness occurred within six months after operation. VAS score decreased from 4.42±1.17 before operation to 0.53±0.61 after operation at 3 months. Mago was improved from 62.63±7.88 before operation to 93.42±5.28 after operation at 3 months. According to Mayo score, 17 cases got excellent results, and 2 cases were good. CONCLUSIONS: Intractable lateral epicondylitis by arthroscopic extracurricular operation based on pressure point, which deal with main extracurricular root cause, could anatomical level is understand easily, field of vision is good and diseased tissue is cleaned up thoroughly, and has obvious curative effect.