| Session: | Poster Session |
| Location: | Poster Area |
| Session Time: | Tuesday, June 26, 15:40 - 17:00 |
| Presentation Time: | Tuesday, June 26, 15:40 - 16:40 |
| Presentation: |
Poster
|
| Paper Title: |
DEEP LEARNING BASED HEVC IN-LOOP FILTERING FOR DECODER QUALITY ENHANCEMENT |
| Authors: |
Shiba Kuanar; University of Texas Arlington, United States | | |
| | Christopher Conly; University of Texas Arlington, United States | | |
| | Kamisetty Rao; University of Texas Arlington, United States | | |
| Abstract: |
High Efficiency Video Coding (HEVC), which is the latest video coding standard currently, achieves up to 50% bit rate reduction compared to previous H.264/AVC standard. While performing the block based video coding, these lossy compression techniques produce various artifacts like blurring, distortion, ringing, and contouring effects on output frames, especially at low bit rates. To reduce those compression artifacts HEVC adopted two post processing filtering technique namely de-blocking filter (DBF) and sample adaptive offset (SAO) on the decoder side. While DBF applies to samples located at block boundaries, SAO nonlinear operation applies adaptively to samples satisfying the gradient based conditions through a lookup table. Again SAO filter corrects the quantization errors by sending edge offset values to decoders. This operation consumes extra signaling bit and becomes an overhead to network. In this paper, we proposed a Convolutional Neural Network (CNN) based architecture for SAO in-loop filtering operation without modifying anything on encoding process. Our experimental results show that our proposed model outperformed previous state-of-the-art models in terms of BD-PSNR (0.408 dB) and BD-BR (3.44%), measured on a widely available standard video sequences. |