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Prior work on image compression has focused on optimizing models to achieve better reconstruction at lower bit rates. These approaches are focused on creating sophisticated architectures that enhance encoder or decoder performance. In some cases, there is the desire to jointly optimize both along with a designed form of entropy encoding. In some instances, these approaches result in the creation of many redundant components, which may or may not be useful.

In this paper, we concentrate on the super-resolution (SR) of compressed screen content video, in an effort to address the real-world challenges by considering the underlying characteristics of screen content. Firstly, we propose a new dataset for the SR of screen content video with different distortion levels. Meanwhile, we design an efficient SR structure that could capture the characteristics of compressed screen content video and manipulate the inner-connections in consecutive compressed low-resolution frames, facilitating the high-quality recovery of the high-resolution counter-part.

Existing compression artifacts reduction methods aim to restore images on pixel-level, which can improves human visual experience. However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks usually by Deep Neural Networks (DNN). One fundamental problem here is whether existing artifacts reduction methods can help DNNs improve the performance of the high-level tasks. In this paper, we find that these methods have limited performance improvements to high-level tasks, even bring negative effects.

Holistic word recognition in handwritten documents is an important research topic in the field of Document Image Analysis. For some applications, given strong language models, it can be more robust and computationally less expensive than character segmentation and recognition. This paper presents HH-CompWordNet, a novel approach to applying a Convolutional Neural Network (CNN) to directly to the DCT coefficients of the compressed domain word images.

Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered and unstructured environments with heavy occlusion, which makes it more challenging to deal with unseen objects. To tackle this problem, we present a synthetic data generation pipeline that randomizes textures via domain randomization to focus on the shape information.

Recently, many deep learning methods have been used to handle single image super-resolution (SISR) tasks and often achieve state-of-the-art performance. From a visual point of view, the results look convincing. Yet, does it mean that those techniques are reliable and robust enough to be implemented in real business cases to enhance the performance of other computer vision tasks?

Face detection and recognition in the wild is currently one of the most interesting and challenging problems. Many algorithms with high performance have already been proposed and applied in real-world applications. However, the problem of detecting and recognising degraded faces from low-quality images and videos mostly remains unsolved. In this paper, we present an algorithm capable of recovering facial features from low-quality videos and images. The resulting output image boosts the performance of existing face detection and recognition algorithms.