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In three-dimensional video system, the texture and depth videos are jointly encoded, and then the Depth Image Based Rendering (DIBR) is utilized to realize view synthesis. However, the compression distortion of texture and depth videos, as well as the disocclusion problem in DIBR degrade the visual quality of the synthesized view. To address this problem, a Two-stream Attention Network (TSAN)-based synthesized view quality enhancement method is proposed for 3D-High Efficiency Video Coding (3D-HEVC) in this article. First, the shortcomings of the view synthesis technique and traditional convolutional neural networks are analyzed. Then, based on these analyses, a TSAN with two information extraction streams is proposed for enhancing the quality of the synthesized view, in which the global information extraction stream learns the contextual information, and the local information extraction stream extracts the texture information from the rendered image. Third, a Multi-Scale Residual Attention Block (MSRAB) is proposed, which can efficiently detect features in different scales, and adaptively refine features by considering interdependencies among spatial dimensions. Extensive experimental results show that the proposed synthesized view quality enhancement method achieves significantly better performance than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Stereoscopic image retargeting aims at converting stereoscopic images to the target resolution adaptively. Different from 2D image retargeting, stereoscopic image retargeting needs to preserve both the shape structure of salient objects and depth consistency of 3D scenes. In this paper, we present a stereoscopic image retargeting method based on deep convolutional neural network to obtain high-quality retargeted images with both object shape preservation and scene depth preservation. First, a cross-attention extraction mechanism is constructed to generate attention map, which contains the valuable attention features of the left and right images and the common attention features between them. Second, since the disparity map can provide accurate depth information of objects in 3D scenes, a disparity-assisted 3D significance map generation module is utilized to further preserve the valuable depth information of stereoscopic images. Finally, in order to predict the retargeted stereoscopic images accurately, an image consistency loss is developed to preserve the geometric structure of salient objects, and a disparity consistency loss is introduced to eliminate depth distortions. Experimental results demonstrate that the proposed deep convolutional neural network can provide favorable stereoscopic image retargeting results. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) video contains plentiful multi-domain correlations, including spatial, temporal, and inter-view correlations. In this paper, a deep multi-domain prediction method is proposed for 3D video coding. Different from previous methods, our proposed method utilizes not only spatial and temporal correlations but also inter-view correlation to obtain a more accurate prediction, and adopts deep convolutional neural networks to effectively fuse multi-domain references. More specifically, a hierarchical prediction mechanism, which includes a spatial-temporal prediction network and a multi-domain prediction network, is designed to overcome the fusion difficulty of multi-domain reference information. Furthermore, a progressive spatial-temporal prediction network and a multi-scale multi-domain prediction network are designed to obtain the spatial-temporal prediction result and multi-domain prediction result respectively. Experimental results show that the proposed method achieves considerable bitrate saving compared with 3D-HEVC. [ABSTRACT FROM AUTHOR]

In this paper, a novel rate-control algorithm based on the region adaptive R- $\lambda $ model is proposed for depth maps coding. First, in order to obtain an accurate rate control for depth maps coding, a modified frame level bit allocation method based on coding bits statistical distribution of depth maps is proposed. Second, considering that different areas in a depth map have an imparity effect on virtual view rendering, the blocks of the depth map are divided into two types, namely, interested blocks for virtual view rending (IBV) and noninterested blocks for virtual view rending (NIBV). Then, two different R- $\lambda $ models are derived for IBV and NIBV, respectively. The optimal bitrates for IBV and NIBV are determined by solving an optimization problem. After that, based on the regional R- $\lambda $ models, the optimal Lagrange multipliers are calculated for both IBV and NIBV. Finally, the largest coding unit (LCU) level rate control is performed by adaptively adjusting the Lagrange multiplier to avoid blocking artifacts and smooth the quality of coding. Experimental results demonstrate that the proposed method can achieve considerable BD-PSNR gains compared with the unified rate-quantization model and conventional R- $\lambda $ model-based algorithms in terms of rendered virtual views quality. [ABSTRACT FROM AUTHOR]

The 3D extension of High Efficiency Video Coding significantly improves the coding efficiency of 3D video at the expense of computational complexity. This paper presents a novel fast mode decision algorithm for depth map coding based on the grayscale similarity and inter-view correlation. First, depth map grayscale similarity is adopted to judge whether the reference frame could assist the coding of the current frame. When the difference in the average grayscale between the co-located coding unit (CU) and the current CU is smaller than the similarity threshold, the depth level of the current CU will be restricted by that of the coded reference CU. Second, the grayscale similarity and inter-view correlation are jointly used for dependent views to achieve early decision on the best prediction unit (PU) mode. The mode decision procedure will be determined early when the co-located CU, which has a grayscale similarity with the current CU, selects Merge or Inter $2N \times 2N$ as the best prediction mode. Moreover, when the corresponding CU in the independent view selects Merge or Inter $2N \times 2N$ as the best prediction mode, the current CU will skip other PU modes checking based on the strong inter-view correlation. Finally, different strategies are proposed for the P-frames and B-frames of dependent views in view of the characteristics of different prediction structures. For B frames, the PU mode information of the coded independent view is utilized as reference to skip the unnecessary mode decision processes. For P frames, the spatial–temporal correlation is considered in the process of early mode decision to determine whether to choose the Merge mode or Inter $2N \times 2N$ as the best mode. Experimental results show that our proposed scheme achieves considerable time saving with negligible degradation of coding performance. [ABSTRACT FROM PUBLISHER]

Traditional image editing techniques cannot be directly used to process stereoscopic media, as extra constraints are required to ensure consistent changes between the left and right images. In this paper, we propose a hybrid warping model for stereoscopic image stitching by combining projective and content-preserving warping. First, a uniform homography algorithm is proposed to prewarp the left and right images, and thus ensure consistent changes. Second, a content-preserving warping is introduced to locally refine alignment and reduce vertical disparities. Finally, a seam-cutting-based algorithm is used to find a blending seam, and the multiband blending algorithm is used to produce the final stitched image. Experimental results show that the proposed method can effectively stitch stereoscopic images, which not only avoids local distortions, but also reduces vertical disparities reasonably. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a pixel fusion-based stereo image retargeting method, which could adaptively retarget stereo images with flexible aspect ratios, simultaneously preserving the depth. Retargeting each image independently by the pixel fusion method ignores the disparity relationship between pixels in the image pair and hence will introduce the distortion of disparity. To address this issue, we advocate to extend the single pixel fusion-based way to be applicable for stereo image pair. First, seams are selected based on the energy function, which simultaneously considers the seam selecting and seam matching. Second, a seam-matching-based matching map is proposed to preserve the disparity relationship between image pair. Then, the scaling factors for the left image are assigned considering both the important object and depth preservation. Subsequently, the scaling factors for the right image are obtained according to the proposed matching map. Based on these scaling factors, the stereo image pair is retargeted with pixel fusion. In contrast to removing pixels to resize image, the way of pixel fusion can obtain more smooth results with less depth distortion. Experimental results demonstrate that our method achieves more preferable qualities in both depth and shape preservation for stereo image retargeting. [ABSTRACT FROM PUBLISHER]

Accurate and high-quality depth maps are required in lots of 3D applications, such as multi-view rendering, 3D reconstruction and 3DTV. However, the resolution of captured depth image is much lower than that of its corresponding color image, which affects its application performance. In this paper, we propose a novel depth map super-resolution (SR) method by taking view synthesis quality into account. The proposed approach mainly includes two technical contributions. First, since the captured low-resolution (LR) depth map may be corrupted by noise and occlusion, we propose a credibility based multi-view depth maps fusion strategy, which considers the view synthesis quality and interview correlation, to refine the LR depth map. Second, we propose a view synthesis quality based trilateral depth-map up-sampling method, which considers depth smoothness, texture similarity and view synthesis quality in the up-sampling filter. Experimental results demonstrate that the proposed method outperforms state-of-the-art depth SR methods for both super-resolved depth maps and synthesized views. Furthermore, the proposed method is robust to noise and achieves promising results under noise-corruption conditions. [ABSTRACT FROM AUTHOR]

This paper presents a novel depth video estimation method, which improves estimation property and refines the temporal consistency and spatial accuracy at the same time. The main idea is to incorporate more useful motion and structure information into depth estimation. First, an adaptive weight is calculated based on the motion information of adjacent frames and attached to a temporal term to update the energy function, thus reducing the matching errors and improving temporal consistency. Second, the depth continuity/discontinuity in spatial domain is properly judged by combining the edges of initial depth map and color segmentation, and used in the refinement strategy. Finally, we evaluate the performance of our algorithm with several public stereoscopic video sequences. Experimental results show that the qualities of depth video and synthesized virtual view are all improved. [ABSTRACT FROM PUBLISHER]

Depth information is an indispensable element in depth image-based rendering (DIBR) for three-dimensional (3-D) display. In this paper, we propose a novel depth sensation enhancement method to address the problems in multiple virtual view rendering. First, as the depth sensation is decreased when rendering intermediate multiple virtual views, the basic principle of depth sensation enhancement is derived according to the number of rendering views. Second, with the increase of the scene complexity, it is difficult to ensure the depth sensation of all neighboring objects. The saliency analysis is adopted to give preferred guarantee to the depth sensation between the salient object and its neighbors. Then, the depth sensation enhancement for multiple virtual view rendering is performed based on a defined energy function built by the number of rendering views and the saliency analysis. Finally, considering the temporal consistency between adjacent frames, the depth sensation enhancement is extended to video applications with a newly designed energy function with energy term of temporal consistency preservation. Experimental results on a public database demonstrate that the proposed method can obtain promising performance in depth sensation. [ABSTRACT FROM PUBLISHER]