Recent down-up sampling video coding methods by CNN-based super resolution (SR) have demonstrated the potential of coding performance improvement. However, these methods generally process shallow features by using only a single layer of convolution, and without high resolution side information, resulting in lack of abundance of features fed into the backbone. In this paper, we propose a luma enhancement module (LEM), which internally consists of a multi-branch structure. Each branch is used for extracting features at different scales, enriching the diversity of shallow features of the model. For the input side information, we use reference picture re-sampling (RPR) images that is not used in the SR model adopted by NNVC-6.0. We consider RPR as a HR frame obtained by up-sampling with traditional conventional filters, which implicitly contains texture information at HR domain.

Focal stack image sequences can be regarded as successive frames of videos, which are densely captured by focusing on a stack of focal planes. This type of data is able to provide focus cues for display technologies. Before the displays on the user side, focal stack video is possibly corrupted during compression, storage and transmission chains, generating error frames on the decoder side. The error regions are difficult to be recovered due to the focal changes among frames. Conventional error concealment methods result in sharpness inconsistency between recovered regions and their spatial adjacent regions. Motivated by this, in this paper, we propose a spatial-focal error concealment scheme specialized for focal stack videos. The spatial adjacent regions around an error region are employed to reveal the prediction relations between error frame and focal adjacent frames. Gaussian blur filtering and Lucy-Richardson deblur filtering are applied to simulate the video focal changes. In this way, the error regions can be well recovered by exploiting the spatial-focal information. Experiment results show that the proposed scheme can achieve the highest objective quality in terms of PSNR and SSIM. It can also obtain the best subjective quality with sharpness consistency in recovered regions and without block effect.

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.

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.