Different wavelengths of light have different attenuation during underwater transmission, and the attenuation of the same wavelength of light is also inconsistent in various waters. This loss of spectral information causes varying degrees of color distortion in underwater images. In this paper, a novel underwater color correction algorithm based on scattering statistics characteristics is proposed. The algorithm is based on a fact that scattering exists in both atmosphere and underwater. Firstly, we statistically analyze three kinds of images and summarize scattering characteristics. Secondly, a novel spectral compensation strategy is proposed to correct color distortion according to scattering characteristics. Finally, compared with the existing underwater image processing algorithms, there are better subjective and objective indicators. The algorithm is robust for a mass of real underwater images.
Depth-image-based-rendering (DIBR) techniques are significant for view synthesis. However, such technique may introduce challenging distortions. Unlike traditional uniform artifacts, the distortions of the synthesized could be local and non-uniform, thus are challenging for traditional image quality assessment metrics. To tackle this problem, aiming at the geometric distortions, a no reference quality assessment for DIBR-synthesized images is proposed in this paper. First, considering that the hue distribution of disoccluded regions is different from that of the natural image, the disoccluded regions are extracted from the hue difference map. The disoccluded regions with different sizes are calculated adaptively by overlapping the hue difference map according to the distortion intensity based on the progressive layer partitioning principle. Second, the artifacts of edges are measured as the distance between the patches at critical regions and their down-sampled versions based on the property of scale invariance. Finally, the perceptual quality is estimate by linearly pooling the scores of two geometric distortions together. The experimental results show that the PLCC, SRCC, RMSE of the proposed model are 0.7613, 0.6965, and 0.4244, respectively. In summary, the proposed metric achieves higher performance, but lower computational complexity than other models.
Research on video quality assessment (VQA) plays a crucial role in improving the efficiency of video coding and the performance of video processing. It is well acknowledged that the motion energy model generates motion energy responses in a middle temporal area by simulating the receptive field of neurons in V1 for the motion perception of the human visual system. Motivated by the biological evidence for the visual motion perception, a VQA method is proposed in this paper, which comprises the motion perception quality index and the spatial index. To be more specific, the motion energy model is applied to evaluate the temporal distortion severity of each frequency component generated from the difference of Gaussian filter bank, which produces the motion perception quality index, and the gradient similarity measure is used to evaluate the spatial distortion of the video sequence to get the spatial quality index. The experimental results of the LIVE, CSIQ, and IVP video databases demonstrate that the random forests regression technique trained by the generated quality indices is highly correspondent to human visual perception and has many significant improvements than comparable well-performing methods. The proposed method has higher consistency with subjective perception and higher generalization capability.
Stereoscopic image quality assessment (IQA) plays a vital role in stereoscopic image/video processing systems. We propose a new quality assessment for stereoscopic image that uses disparity-compensated view filtering (DCVF). First, because a stereoscopic image is composed of different frequency components, DCVF is designed to decompose it into high-pass and low-pass components. Then, the qualities of different frequency components are acquired according to their phase congruency and coefficient distribution characteristics. Finally, support vector regression is utilized to establish a mapping model between the component qualities and subjective qualities, and stereoscopic image quality is calculated using this mapping model. Experiments on the LIVE 3-D IQA database and NBU 3-D IQA databases demonstrate that the proposed method can evaluate stereoscopic image quality accurately. Compared with several state-of-the-art quality assessment methods, the proposed method is more consistent with human perception.
KEYWORDS: Visualization, Databases, 3D modeling, Data modeling, Performance modeling, Visual process modeling, 3D image processing, Spatial frequencies, Molybdenum, 3D visualizations
Three-dimensional (3-D) visual comfort assessment (VCA) is a particularly important and challenging topic, which involves automatically predicting the degree of visual comfort in line with human subjective judgment. State-of-the-art VCA models typically focus on minimizing the distance between predicted visual comfort scores and subjective mean opinion scores (MOSs) by training a regression model. However, obtaining precise MOSs is often expensive and time-consuming, which greatly constrains the extension of existing MOS-aware VCA models. This study is inspired by the fact that humans tend to conduct a preference judgment between two stereoscopic images in terms of visual comfort. We propose to train a robust VCA model on a set of preference labels instead of MOSs. The preference label, representing the relative visual comfort of preference stereoscopic image pairs (PSIPs), is generally precise and can be obtained at much lower cost compared with MOS. More specifically, some representative stereoscopic images are first selected to generate the PSIP training set. Then, we use a support vector machine to learn a preference classification model by taking a differential feature vector and the corresponding preference label of each PSIP as input. Finally, given a testing sample, by considering a full-round paired comparison with all the selected representative stereoscopic images, the visual comfort score can be estimated via a simple linear mapping strategy. Experimental results on our newly built 3-D image database demonstrate that the proposed method can achieve a better performance compared with the models trained on MOSs.
Since stereoscopic images provide observers with both realistic and discomfort viewing experience, it is necessary to
investigate the determinants of visual discomfort. By considering that foreground object draws most attention when
human observing stereoscopic images. This paper proposes a new foreground object based visual comfort assessment
(VCA) metric. In the first place, a suitable segmentation method is applied to disparity map and then the foreground
object is ascertained as the one having the biggest average disparity. In the second place, three visual features being
average disparity, average width and spatial complexity of foreground object are computed from the perspective of
visual attention. Nevertheless, object’s width and complexity do not consistently influence the perception of visual
comfort in comparison with disparity. In accordance with this psychological phenomenon, we divide the whole images
into four categories on the basis of different disparity and width, and exert four different models to more precisely
predict its visual comfort in the third place. Experimental results show that the proposed VCA metric outperformance
other existing metrics and can achieve a high consistency between objective and subjective visual comfort scores. The
Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are over
0.84 and 0.82, respectively.
In multi-view video system, multiple video plus depth is main data format of 3D scene representation. Continuous virtual
views can be generated by using depth image based rendering (DIBR) technique. DIBR process includes geometric
mapping, hole filling and merging. Unique weights, inversely proportional to the distance between the virtual and real
cameras, are used to merge the virtual views. However, the weights might not the optimal ones in terms of virtual view
quality. In this paper, a novel virtual view merging algorithm is proposed. In the proposed algorithm, machine learning
method is utilized to establish an optimal weight model. In the model, color, depth, color gradient and sequence
parameters are taken into consideration. Firstly, we render the same virtual view from left and right views, and select the
training samples by using a threshold. Then, the eigenvalues of the samples are extracted and the optimal merging
weights are calculated as training labels. Finally, support vector classifier (SVC) is adopted to establish the model which
is used for guiding virtual views rendering. Experimental results show that the proposed method can improve the quality
of virtual views for most sequences. Especially, it is effective in the case of large distance between the virtual and real
cameras. And compared to the original method of virtual view synthesis, the proposed method can obtain more than
0.1dB gain for some sequences.
KEYWORDS: Video, Video processing, Video coding, Computer programming, Video compression, Cameras, Optical filters, Gaussian filters, Information science, 3D vision
In free viewpoint video system, the color and the corresponding depth video are utilized to synthesize the virtual views
by depth image based rendering (DIBR) technique. Hence, high quality of depth videos is a prerequisite for high quality
of virtual views. However, depth variation, caused by scene variance and limited depth capturing technologies, may
increase the encoding bitrate of depth videos and decrease the quality of virtual views. To tackle these problems, a depth
preprocess method based on smoothing the texture and abrupt changes of depth videos is proposed to increase the
accuracy of depth videos in this paper. Firstly, a bilateral filter is adopted to smooth the whole depth videos and protect
the edge of depth videos at the same time. Secondly, abrupt variation is detected by a threshold calculated according to
the camera parameter of each video sequence. Holes of virtual views occur when the depth values of left view change
obviously from low to high in horizontal direction or the depth values of right view change obviously from high to low.
So for the left view, depth value difference in left side gradually becomes smaller where it is greater than the thresholds.
And then, in right side of right view is processed likewise. Experimental results show that the proposed method can
averagely reduce the encoding bitrate by 25% while the quality of the synthesized virtual views can be improve by
0.39dB on average compared with using original depth videos. The subjective quality improvement is also achieved.
KEYWORDS: Digital watermarking, Image restoration, Detection and tracking algorithms, Image quality, 3D image processing, Image processing, Image compression, Multimedia, Visualization, Signal to noise ratio
We propose a new watermarking algorithm for stereoscopic image tamper detection and self-recovery in three-dimensional multimedia services. Initially, left and right views of stereoscopic image are divided into nonoverlapping 2×2 blocks in order to improve the accuracy of tamper localization in an image. As the left and right views of a stereoscopic image are not independent from each other but have an inter-view relationship, every block of a stereoscopic image is classified into matching block or nonmatching block and then block disparities are obtained. Both matching blocks in the left and right views have similar pixel values, so that fewer bits are allocated for recovery watermark generation, which can increase the quality of watermarked stereoscopic images. A hierarchical tamper-detection strategy with a four-level checkup is presented to improve the accuracy of tamper localization. Additionally, two copies of block (matching block and nonmatching block) information are embedded into the stereoscopic image, and it assures the quality of tampered recovery. For the nonmatching block recovery, two copies of the partner block are embedded into their chaotic mapping blocks, which supply the second chance for tamper recovery. For the matching block recovery, the inter-view relationship between tampers of left and right views supplies the third chance for tamper recovery. Experimental results show that the proposed algorithm can not only detect and locate tampers in stereoscopic image more accurately but also recover the tampered regions better, compared with other algorithms.
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