Search Results for author:
Found 5 papers, 2 papers with code
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network
In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos.
Light field Rectification based on relative pose estimation
Specifically, the proposed method aligns two LFs captured by two hand-held LF cameras with a random relative pose, and extracts the corresponding row-aligned sub-aperture images (SAIs) to obtain an LF with a large baseline.
DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video
Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos.
DVC-P: Deep Video Compression with Perceptual Optimizations
In this paper, we introduce deep video compression with perceptual optimizations (DVC-P), which aims at increasing perceptual quality of decoded videos.
A Two-step Calibration Method for Unfocused Light Field Camera Based on Projection Model Analysis
The proposed method is able to reuse traditional camera calibration methods for the direction parameter set.
