Immersive video allows viewers to freely switch the viewpoints. The intensity of realistic experience greatly relies on the quality of synthesized depth maps. However, there exist distorted regions due to inaccurate depth estimation or compression.

Object detection under low-light surveillance is a crucial problem that less efforts have been made on it. In this paper, we proposed a hybrid method that jointly use enhancement and object detection for the above challenge, namely Dedark+Detection. In this method, the low-light surveillance video is processed by the proposed de-dark method, and the video can thus be converted to appearance under normal lighting condition. This enhancement bring more benefits to the subsequent stage of object detection. After that, an object detection network is trained on the enhanced dataset for practical applications under low-light surveillance. Experiments are performed on 18 low-light surveillance video test sequences, and superior performance can be found when comparing to state-of-the-arts.

Focal stack is an intermediate data representation obtained by projecting 4D light field (LF) in z-dimension. This kind of representation is fundamental for future interactive and immersive visual applications. However, focal stack images are a series of samples focused at varying depths of static scenes, which yields considerable redundancy among them. In this paper, we propose a Gaussian guided inter prediction model to eliminate the visual redundancy. In our work, the effect of the varying focus distance on plenoptic imaging system is characterized by the point spread function (PSF), and we propose a simplified Gaussian-like PSF to fit this characteristic according to the features of focal stack images. After that, Gaussian guided motion estimation and motion compensation are both implemented in this model. Experimental results show that our model can achieve smaller residual distribution. There are 10.33% bit rate saving and 0.397 dB PSNR gain on average in three configurations compared with HEVC anchor. Particularly, it brings about up to 16.60% bit rate saving with 0.649 dB PSNR increment in Low Delay P configuration.

Asymmetric coding methodology for multi-view video plus depth is a promising technique for future three-dimensional and multi-view driven visual applications for its superior coding performance in bandwidth limited conditions. Since the depth video suffers from asymmetric distortions corresponding to viewpoint, it's a challenge in smooth and quality consistent content based interaction. To solve this challenge, we propose a residual learning framework to enhance the quality of compression distorted multi-view depth video. In this work, we exploit the correlation between viewpoints to restore the target viewpoint depth maps by using multi-modality priors, which are depth maps from adjacent viewpoints with better quality and color frames in the same viewpoint. A residual network is designed to fully exploit the contribution from these priors. Experimental results show the superiority of our framework in the quality improvement on both decoded depth video and synthesized virtual viewpoint images.

Light field is the most prospective technology for interactive and immersive visual applications. and light field image is an intermediate data format that demands a large amount of storage space and higher transmission bandwidth. Therefore, compression of light field images is highly desired for further applications. In this paper, we propose a co-saliency guided bit allocation scheme with constraints of consistency among sub-aperture images. Firstly, saliency is jointly detected on color and depth images of sub-aperture by improving our previous model. The obtained pixel-wise co-saliency map is converted into block-wise via K-means clustering. In this way, the saliency weight of each coding tree unit (CTU) can be calculated. Then, target bits of each CTU are initially determined by the weight of each block. The allocation is adjusted dynamically under the guidance of co-saliency map and the image texture complexity. The experimental results show that BD-PSNR of 0.384 dB can be achieved for the salient region at the cost of less than 0.107 dB decrease for the whole image compared to HTM anchor. Moreover, subjective quality of proposed scheme outperforms the anchor for the salient region, and there is no noticeable distortion for non-salient region.

In this paper, we propose a method of illumination attribute coding method for virtual reality broadcasting system. As for the virtual reality content, it is captured with local illumination variations. Our method is motivated by the Phong illumination model, and illumination attribute is extracted from images and then an illumination reference is synthesized with higher correlation to the current image.