Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media. In order to enhance and restore such images, we first estimate ambient light using the depth-dependent color change. Then, via calculating the difference between the observed intensity and the ambient light, which we call the scene ambient light differential, scene transmission can be estimated. Additionally, adaptive color correction is incorporated into the image formation model (IFM) for removing color casts while restoring contrast. Experimental results on various degraded images demonstrate the new method outperforms other IFM-based methods subjectively and objectively. Our approach can be interpreted as a generalization of the common dark channel prior (DCP) approach to image restoration, and our method reduces to several DCP variants for different special cases of ambient lighting and turbid medium conditions.
With the development of immersive video, the delivery and storage of 3D content have become important research areas. While compression methods for meshes and point clouds, the two main representations for 3D content, are actively studied, there are few studies of their perceptual compression quality and none that consider observation distance. In this paper, we study the perceptual quality of compressed 3D sequences, for both point cloud compression and mesh-based compression. We explore the impact of bit rate and observation distance on perceptual quality. Evaluation of perceptual quality is carried out both by collecting viewer opinion scores of the compressed sequences separately, and with a side-by-side comparison. A functional model for mesh and point cloud compression quality is estimated to predict Mean Opinion Score (MOS) which yields high Pearson correlation and rank correlation scores with measured MOS.
The semicircular bending (SCB) test has been shown to possess several advantages over other tests in characterizing asphalt mixtures in previous studies. This research study evaluates the SCB test for determining the tensile strength and stiffness modulus of the paving material with numerical simulation and laboratory experimentation. An analytical model describing the tensile stress at the middle point of the lower surface of the specimen in the SCB test was developed based on the plane assumption in mechanics of materials. Analysis using the finite element method indicated that the error induced by the model was within 2 %. Laboratory experiment carried out on three types of asphalt mixtures at various temperatures showed that the strength of the material by the SCB test was nearly 50 % higher on average than that by the flexural beam bending (FBB) test due to such factors as complexities in stress and strain states as well as nonlinearity and viscoelasticity of the material. Laboratory experiment also showed that the stiffness moduli for 10–40 % of maximum load from the FBB test and from the SCB test were in a well-defined linear relationship with differences less than 10 %. In addition, based on finite element analysis, a practical approach for determining stiffness modulus of asphalt mixtures using deflection at the middle point of the lower surface of the specimen in the SCB test was established.
With the development of immersive video, the quality of compressed 3D content has become an important issue. Video-based Point Cloud Compression (V-PCC) is a popular compression method for point cloud sequences; it achieves the highest quality among MPEG proposals. Compressed point clouds suffer from various artifacts when a high quantization parameter (QP) is used. Examining the causes and types of V-PCC artifacts that occur, we propose a framework to remove the highly noticeable outlier and crack artifacts caused by V-PCC so as to improve compressed point cloud visual quality. A subjective experiment showed that our approach provides significantly improved visual quality, and the improvement becomes more obvious with increasing QP values. Objective evaluation with point-to-point Mean Squared Error (p2p-MSE) shows our proposed method can improve point cloud quality and provides competitive results with lower complexity compared with other methods for point cloud outlier removal and inpainting.
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