Demand for efficient image transmission and storage is increasing rapidly because of the continuing growth of multimedia technology and VR and AR applications. In this paper, we proposed an image compression method based on the recognition of importance of regions in images. As not all the information in an image is equally useful, we can identify important regions in an image for high fidelity compression and accept a comparatively more lossy compression about less important regions of the image. First, we segment images to two parts, namely, foreground and background, where the foreground represents the more important component and the background is of less importance. Second, we apply optimal mass transportation mapping in a GAN (generative adversarial network) framework to both the foreground and background to magnify the foreground and shrink the background while keeping the shape and total image area unchanged. As a result, in the processed image, the ratio of foreground to background is larger than the corrresponding ratio in the original image. This ratio is controllable in our process, giving users the ability to control the degree of compression. The GAN-processed image is then used for compression. To restore the image, we apply a GAN model to the compressed image and recover the ratio of foreground and background using an optimal mass transportation map. Test results show that our method is highly effective in reconstructing detail of important components in compressed images while achieving a high compression ratio.

With the huge development of stereoscopic video technology, the research of stereoscopic video quality assessment (SVQA) has become very important for promoting the development of stereoscopic video system. These years, many SVQA methods based on convolutional neural network (CNN) have emerged. In this paper, we proposed a multi-scale feature-guided 3D convolutional neural network for SVQA which not only use 3D convolution to capture spatio-temporal features but also aggregate multi-scale information by a new multi-scale unit. Besides, we employ a multi-stage growing attention mechanism in this network to learn more critical deep semantic information. The proposed method is tested on two public stereoscopic video quality datasets, and the result shows that this method correlates highly with human visual perception and outperforms state-of-the-art methods by a large margin.

Stereoscopic video quality assessment (SVQA) is of great importance to promote the development of the stereoscopic video industry. In this paper, we propose a three-branch multi-level binocular fusion convolutional neural network (MBFNet) which is highly consistent with human visual perception. Our network mainly includes three innovative structures. Firstly, we construct a multi-scale cross-dimension attention module (MSCAM) on the left and right branches to capture more critical semantic information. Then, we design a multi-level binocular fusion unit (MBFU) to fuse the features from left and right branches adaptively. Besides, a disparity compensation branch (DCB) containing an enhancement unit (EU) is added to provide disparity feature. The experimental results show that the proposed method is superior to other existing SVQA methods with state-of-the-art performance.

In recent years, deep learning has achieved significant progress in many respects. However, unlike other research fields with millions of labeled data such as image recognition, only several thousand labeled images are available in image quality assessment (IQA) field for deep learning, which heavily hinders the development and application for IQA. To tackle this problem, in this paper, we proposed an error self-learning semi-supervised method for no-reference (NR) IQA (ESSIQA), which is based on deep learning. We employed an advanced full reference (FR) IQA method to expand databases and supervise the training of network. In addition, the network outputs of expanding images were used as proxy labels replacing errors between subjective scores and objective scores to achieve error self-learning. Two weights of error back propagation were designed to reduce the impact of inaccurate outputs. The experimental results show that the proposed method yielded comparative effect.

In this report, we first point out and analyse an error in the implementation of the pre-encoding logic in the LOP module proposed in [1], and then present a modification method.