Video super-resolution(VSR) upscales a low-resolution video to the higher one. Most applications require compression of the super-resolved video due to limited internet bandwidth and storage capacity. However, most studies on VSR techniques have focused only on improving image quality, ignoring the impact of the compression process on visual quality. Consequently, even a VSR with good visual quality has a risk of significant loss of quality when serviced online or stored as a file. To address this problem, we propose an encoding-aware VSR framework. In the framework, we created a differentiable virtual codec to estimate the bit rate and used it for the loss function, which optimizes the super-resolved videos by considering the rate-distortion trade-off relationship and eventually leads to the prevention of visual quality degradation. According to the results, our real-time VSR model for x4 upscaling, trained with 1,191K parameters, yields a maximum gain of 13.2% over state-of-the-art VSR models based on the Bjøntegaard delta rate.

Mobile apps offer a variety of features that greatly enhance user experience. However, users still often find it difficult to use mobile apps in the way they want. For example, it is not easy to use multiple apps simultaneously on a small screen of a smartphone. In this paper, we present A-Mash, a mobile platform that aims to simplify the way of interacting with multiple apps concurrently to the level of using a single app only. A key feature of A-Mash is that users can mash up the UIs of different existing mobile apps on a single screen according to their preferences. To this end, A-Mash 1) extracts UIs from unmodified existing apps (dynamic UI extraction) and 2) embeds extracted UIs from different apps into a single wrapper app (cross-process UI embedding), while 3) making all these processes hidden from the users (transparent execution environment). To the best of our knowledge, A-Mash is the first work to enable UIs of different unmodified legacy apps to seamlessly integrate and synchronize on a single screen, providing an illusion as if they were developed as a single app. A-Mash offers great potential for a number of useful usage scenarios. For instance, a user can mashup UIs of different IoT administration apps to create an all-in-one IoT device controller or one can mashup today's headlines from different news and magazine apps to craft one's own news headline collection. In addition, A-Mash can be extended to an AR space, in which users can map UI elements of different mobile apps to physical objects inside their AR scenes. Our evaluation of the A-Mash prototype implemented in Android OS demonstrates that A-Mash successfully supports the mashup of various existing mobile apps with little or no performance bottleneck. We also conducted in-depth user studies to assess the effectiveness of the A-Mash in real-world use cases.

Image filtering is a fundamental preprocessing step for accurate, robust computer vision applications such as image segmentation, object classification, and reconstruction. However, many convolutional neural network (CNN)‐based methods tend to lose significant edge information in the output layer, and generate undesired artefacts in the feature extraction layers. This study presents a deep CNN model for edge‐aware image filtering. The proposed network model consists of three sub‐networks: (i) feature extraction, (ii) convolution artefact removal, and (iii) structure extraction networks. The proposed network model has an end‐to‐end trainable architecture that does not need any post‐processing steps. Especially, the structure extraction network can successfully preserve significant edges. The proposed filter outperforms state‐of‐the‐art denoising filters in terms of both objective and subjective measures, and can be used for various image enhancement and restoration problems such as edge‐preserving smoothing, image denoising, deblurring, and deblocking.

Although many virtual environment (VE) technologies such as the four-screen CAVE™ are described as immersive, users can still perceive distractions from the real world. This exposure to real-world distraction may reduce users' sense of presence, and if presence is correlated with performance as some have claimed, the real-world distractions may also hinder performance. Thus, VE designers may want to consider ways to reduce real-world distraction. This paper presents an experiment to investigate the effect of reduced visual stimulus in the peripheral area on user performance and the usability of an immersive VE. We carefully designed three tasks that cause different levels of awareness of the real-world distraction. Using these tasks, we evaluated users' performance and preference in two conditions. The low-stimulus condition was created by hanging a black cloth across the missing back wall of a CAVE. The high-stimulus condition was created by projected animations and real human motion outside the CAVE. The experiments show that reduced distraction may have a positive or negative effect on user performance, depending on the specific tasks and environments.