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In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic (textual) content. However, usually screen content images not only consist of synthetic but also pictorial (natural) regions. These images require diverse models of probability distributions to be optimally compressed.

Visual Question Answering (VQA) stands to benefit from the boost of increasingly sophisticated Pretrained Language Model (PLM) and Computer Vision-based models. In particular, many language modality studies have been conducted using image captioning or question generation with the knowledge ground of PLM in terms of data augmentation. However, image generation of VQA has been implemented in a limited way to modify only certain parts of the original image in order to control the quality and uncertainty.

In this paper, we propose a novel low-rank based non-local image denoising method for HEVC video compression with the strategy of gathering non-local patches in the rectified domain. Owing to the irreversible quantization, image compression can be considered as adding noises into the original image, causing the distortion between the original image and the de-compressed image.

The evaluation of the quality of gaming content, with low-complexity and low-delay approaches is a major challenge raised by the emerging gaming video streaming and cloud-gaming services. Considering two existing and a newly created gaming databases this paper confirms that some low-complexity metrics match well with subjective scores when considering usual correlation indicators. It is however argued such a result is insufficient: gaming content suffers from sudden large quality drops that these indicators do not capture.

Unsupervised domain adaptation has shown promising results in leveraging synthetic (source) images for semantic segmentation of real (target) images. One key issue is how to align data distributions between the source and target domains. Adversarial learning has been applied to align these distributions. However, most existing approaches focus on aligning the output distributions related to image (global) segmentation. Such global alignment may not result in effective alignment due to the inherent high dimensionality feature space involved in the alignment.