In this study, we address the emerging necessity of converting Standard
Dynamic Range Television (SDRTV) content into High Dynamic Range Television
(HDRTV) in light of the limited number of native HDRTV content. A principal
technical challenge in this conversion is the exacerbation of coding artifacts
inherent in SDRTV, which detrimentally impacts the quality of the resulting
HDRTV. To address this issue, our method introduces a novel approach that
conceptualizes the SDRTV-to-HDRTV conversion as a composite task involving dual
degradation restoration. This encompasses inverse tone mapping in conjunction
with video restoration. We propose Dual Inversion Downgraded SDRTV to HDRTV
Network (DIDNet), which can accurately perform inverse tone mapping while
preventing encoding artifacts from being amplified, thereby significantly
improving visual quality. DIDNet integrates an intermediate auxiliary loss
function to effectively separate the dual degradation restoration tasks and
efficient learning of both artifact reduction and inverse tone mapping during
end-to-end training. Additionally, DIDNet introduces a spatio-temporal feature
alignment module for video frame fusion, which augments texture quality and
reduces artifacts. The architecture further includes a dual-modulation
convolution mechanism for optimized inverse tone mapping. Recognizing the
richer texture and high-frequency information in HDRTV compared to SDRTV, we
further introduce a wavelet attention module to enhance frequency features. Our
approach demonstrates marked superiority over existing state-of-the-art
techniques in terms of quantitative performance and visual quality.
In this study, the authors address the challenge of converting Standard Dynamic Range Television (SDRTV) content into High Dynamic Range Television (HDRTV) due to the limited availability of native HDRTV content. The conversion process poses technical challenges, particularly in relation to coding artifacts that are inherent in SDRTV and negatively impact the quality of the resulting HDRTV.
The authors propose a novel approach called Dual Inversion Downgraded SDRTV to HDRTV Network (DIDNet) to address this issue. DIDNet conceptualizes the SDRTV-to-HDRTV conversion as a composite task involving dual degradation restoration, combining inverse tone mapping and video restoration.
To achieve accurate inverse tone mapping while preventing the amplification of encoding artifacts, DIDNet incorporates an intermediate auxiliary loss function. This helps effectively separate the tasks of artifact reduction and inverse tone mapping, allowing for efficient learning during end-to-end training.
DIDNet also includes a spatio-temporal feature alignment module for video frame fusion, which enhances texture quality and reduces artifacts. Furthermore, a dual-modulation convolution mechanism is introduced for optimized inverse tone mapping.
Recognizing the richer texture and high-frequency information present in HDRTV compared to SDRTV, the authors introduce a wavelet attention module to enhance frequency features.
The authors demonstrate the superiority of their approach over existing state-of-the-art techniques in terms of both quantitative performance and visual quality.
Multi-Disciplinary Nature of the Concepts
This study involves the integration of concepts from various disciplines, highlighting its multi-disciplinary nature. The authors combine techniques from image processing, computer vision, and machine learning to tackle the challenges of converting SDRTV to HDRTV. The use of neural networks, loss functions, convolution mechanisms, and attention modules showcases the convergence of these disciplines in the field of multimedia information systems.
Relation to Multimedia Information Systems
Multimedia information systems encompass the management, organization, and retrieval of multimedia content. The conversion of SDRTV to HDRTV is crucial in enhancing the visual quality and user experience of multimedia content. By addressing the technical challenges of this conversion and improving the visual quality of HDRTV, this study contributes to the wider field of multimedia information systems.
Relation to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The conversion of SDRTV to HDRTV has implications for various fields that utilize animations, artificial reality, augmented reality, and virtual realities. High-quality visual content is essential in these domains to provide immersive and realistic experiences. By improving the visual quality of HDRTV, this study contributes to enhancing the realism and immersion in animations, artificial reality, augmented reality, and virtual realities.