Multidirectional motion estimation algorithm for frame rate up-conversion

In this paper, we propose a new motion estimation algorithm for motion-compensated frame rate up-conversion. To reduce computational complexity, it constructs a hierarchical structure using Gaussian image pyramid and unidirectionally estimates motion vectors (MVs) between two original frames at the top level. Since the accuracy of the motion vector may deteriorate at low resolution, each block has five candidates for motion vector so that disadvantages of hierarchical motion estimation are reduced. After motion estimation (ME), MVs of bottom level are corrected based on the sum of absolute difference (SAD) values of the blocks. All unidirectional MVs are assigned to bidirectional MVs, and new frames are interpolated using these MVs. As a result, proposed algorithm shows up to 1.3 dB increase in PSNR compared with conventional algorithm with low computational complexity, and it shows clearer images than conventional algorithms

AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results

Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results. From low-frame-rate (15 fps) video sequences, the challenge participants are asked to submit higher-frame-rate (60 fps) video sequences by estimating temporally intermediate frames. We employ the REDS VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. The competition had 62 registered participants, and a total of 8 teams competed in the final testing phase. The challenge winning methods achieve the state-of-the-art in video temporal super-resolution.N

NTIRE 2019 Challenge on Real Image Denoising: Methods and Results

This paper reviews the NTIRE 2019 challenge on real image denoising with focus on the proposed methods and their results. The challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer- pattern raw-RGB and (2) the standard RGB (sRGB) color spaces. The tracks had 216 and 220 registered participants, respectively. A total of 15 teams, proposing 17 methods, competed in the final phase of the challenge. The proposed methods by the 15 teams represent the current state-of-the- art performance in image denoising targeting real noisy im- ages