Name
Abstract | ||
|---|---|---|
In this paper, a low bit-rate compressed image quality enhancement framework is presented. A recent image/video coding method and a deep learning based quality enhancement method are integrated to improve the perceptual quality of compressed images. The proposed architecture is designed to reduce the coding artifact and restore the blurred texture details. The experimental results presents that the proposed framework yields a 33% improvement in the Perceptual Index score which is consistent with visual evaluation on a sample of results. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPRW50498.2020.00076 | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | DocType | ISSN |
perceptual quality enhancement,low bit-rate compressed images,low bit-rate compressed image quality enhancement framework,deep learning based quality enhancement method,perceptual index score | Conference | 2160-7508 |
ISBN | Citations | PageRank |
978-1-7281-9361-8 | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Younhee Kim | 1 | 112 | 11.57 |
| Seunghyun Cho | 2 | 2 | 1.78 |
| Joo-Young Lee | 3 | 77 | 12.36 |
| Seyoon Jeong | 4 | 55 | 13.22 |
| Jin Soo Choi | 5 | 110 | 18.91 |
| Jihoon Do | 6 | 0 | 0.34 |