A New Journey from SDRTV to HDRTV
- URL: http://arxiv.org/abs/2108.07978v1
- Date: Wed, 18 Aug 2021 05:17:08 GMT
- Title: A New Journey from SDRTV to HDRTV
- Authors: Xiangyu Chen, Zhengwen Zhang, Jimmy S. Ren, Lynhoo Tian, Yu Qiao, Chao
Dong
- Abstract summary: We conduct an analysis of SDRTV-to-TV task by modeling the formation of SDRTV/TV content.
We present a lightweight network that utilizes global statistics as guidance to conduct image-adaptive color mapping.
- Score: 36.58487005995048
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nowadays modern displays are capable to render video content with high
dynamic range (HDR) and wide color gamut (WCG). However, most available
resources are still in standard dynamic range (SDR). Therefore, there is an
urgent demand to transform existing SDR-TV contents into their HDR-TV versions.
In this paper, we conduct an analysis of SDRTV-to-HDRTV task by modeling the
formation of SDRTV/HDRTV content. Base on the analysis, we propose a three-step
solution pipeline including adaptive global color mapping, local enhancement
and highlight generation. Moreover, the above analysis inspires us to present a
lightweight network that utilizes global statistics as guidance to conduct
image-adaptive color mapping. In addition, we construct a dataset using HDR
videos in HDR10 standard, named HDRTV1K, and select five metrics to evaluate
the results of SDRTV-to-HDRTV algorithms. Furthermore, our final results
achieve state-of-the-art performance in quantitative comparisons and visual
quality. The code and dataset are available at
https://github.com/chxy95/HDRTVNet.
Related papers
- Beyond Feature Mapping GAP: Integrating Real HDRTV Priors for Superior SDRTV-to-HDRTV Conversion [22.78096367667505]
The rise of HDR-WCG display devices has highlighted the need to convert SDRTV to HDRTV.
Existing methods primarily focus on designing neural networks to learn a single-style mapping from SDRTV to HDRTV.
We propose a novel method for SDRTV to HDRTV conversion guided by real HDRTV priors.
arXiv Detail & Related papers (2024-11-16T11:20:29Z) - HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting [76.5908492298286]
Existing HDR NVS methods are mainly based on NeRF.
They suffer from long training time and slow inference speed.
We propose a new framework, High Dynamic Range Gaussian Splatting (-GS)
arXiv Detail & Related papers (2024-05-24T00:46:58Z) - Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation [54.64335350932855]
We propose a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging.
Our proposed Asyn system integrates the Dynamic Vision Sensors (DVS) with a set of LCD panels.
The LCD panels modulate the irradiance incident upon the DVS by altering their transparency, thereby triggering the pixel-independent event streams.
arXiv Detail & Related papers (2024-03-14T13:45:09Z) - HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment [36.1179702443845]
We introduce HIDRO-VQA, a no-reference (NR) video quality assessment model designed to provide precise quality evaluations of High Dynamic Range () videos.
Our findings demonstrate that self-supervised pre-trained neural networks can be further fine-tuned in a self-supervised setting to achieve state-of-the-art performance.
Our algorithm can be extended to the Full Reference VQA setting, also achieving state-of-the-art performance.
arXiv Detail & Related papers (2023-11-18T12:33:19Z) - Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation [51.26219245226384]
Modern displays are capable of rendering video content with high dynamic range (WCG) and wide color gamut (SDR)
The majority of available resources are still in standard dynamic range (SDR)
We define and analyze the SDRTV-to-TV task by modeling the formation of SDRTV/TV content.
Our method is primarily designed for ultra-high-definition TV content and is therefore effective and lightweight for processing 4K resolution images.
arXiv Detail & Related papers (2023-09-08T02:50:54Z) - HDR or SDR? A Subjective and Objective Study of Scaled and Compressed
Videos [36.33823452846196]
We conducted a large-scale study of human perceptual quality judgments of High Dynamic Range (SDR) and Standard Dynamic Range (SDR) videos.
We found subject preference of HDR versus SDR depends heavily on the display device, as well as on resolution scaling and resolution.
arXiv Detail & Related papers (2023-04-25T21:43:37Z) - Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and
Degradation Models [4.0336006284433665]
In media industry, the demand of SDR-to-TV up-conversion arises when users possess HDR-WCG (high dynamic range-wide color gamut)
Current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience.
We propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR models.
arXiv Detail & Related papers (2023-03-23T04:40:33Z) - HDR-NeRF: High Dynamic Range Neural Radiance Fields [70.80920996881113]
We present High Dynamic Range Neural Radiance Fields (-NeRF) to recover an HDR radiance field from a set of low dynamic range (LDR) views with different exposures.
We are able to generate both novel HDR views and novel LDR views under different exposures.
arXiv Detail & Related papers (2021-11-29T11:06:39Z) - HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with
Large Motions [62.44802076971331]
We propose a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images.
By incorporating adversarial learning, our method is able to produce faithful information in the regions with missing content.
arXiv Detail & Related papers (2020-07-03T11:42:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.