Objective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos

核心概念

The authors developed a novel objective quality model called Cut-FUNQUE that can accurately predict the visual quality of tone-mapped and compressed HDR videos.

要約

The content discusses the challenges in streaming high dynamic range (HDR) videos to a broad consumer base that utilizes legacy standard dynamic range (SDR) displays. To address this, HDR videos must be tone-mapped before streaming, which can introduce various distortions. Additionally, the necessary lossy compression for streaming further degrades the video quality.
The authors propose a new objective quality model called Cut-FUNQUE that can accurately predict the visual quality of tone-mapped and compressed HDR videos. Key aspects of Cut-FUNQUE include:

A novel perceptually uniform color encoding function (PUColor) to represent both HDR and SDR color stimuli in a common domain, enabling meaningful comparison across dynamic ranges.

A binned-weighting approach to separately handle image regions with different visual characteristics like brightness, contrast, and temporal complexity.

Novel statistical similarity measures to overcome the limitations of pixel-wise comparisons across dynamic ranges.

Cut-FUNQUE is evaluated on the recently introduced LIVE Tone-Mapped HDR (LIVE-TMHDR) subjective database and is shown to achieve state-of-the-art accuracy in predicting the visual quality of tone-mapped and compressed HDR videos.

統計

The Human Visual System (HVS) can perceive luminances ranging from 10^-6 to 10^8 nits, while conventional imaging and display systems are limited to up to about 100 nits.
Contemporary HDR standards like ITU BT. 2100 can capture luminances from 10^-4 to 10^4 nits and a wide color gamut.
A majority of budget-friendly HDR displays fall short of the 1000 nits minimum defined in the BT. 2100 standard, necessitating tone-mapping of HDR videos for broader accessibility.

引用

"To ensure accessibility of HDR video content to a broader consumer base, it becomes essential to 'down-convert' them to the SDR range, a process commonly referred to as 'tone-mapping.'"
"The primary challenge that is endemic to the field of tone-mapped video quality assessment is identifying a common domain in which both HDR and SDR may be well represented."

抽出されたキーインサイト

Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos

by Abhinau K. V... 場所 arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13452.pdf

深掘り質問

How can the proposed PUColor encoding function be extended to represent other color spaces beyond the opponent color channels used in this work

The proposed PUColor encoding function can be extended to represent other color spaces beyond the opponent color channels by adapting the transformation matrix used in the encoding process. To represent color spaces like CIELAB or CIE XYZ, the transformation matrix can be modified to align with the specific color space's characteristics. For example, for CIELAB, the transformation matrix can be adjusted to account for the perceptual uniformity of the L*, a*, and b* channels. By incorporating the appropriate transformation matrix and adjusting the encoding function parameters, the PUColor encoding function can be tailored to accurately represent a wide range of color spaces, providing a versatile and comprehensive color encoding solution.

What are the potential limitations of the binned-weighting approach, and how could it be further improved to better capture the complex interactions between tone-mapping and compression distortions

The binned-weighting approach, while effective in capturing the variations in luminance, spatial complexity, and temporal complexity of different regions in the frame, may have limitations in fully capturing the complex interactions between tone-mapping and compression distortions. One potential limitation is the reliance on predefined bins and weights, which may not always accurately reflect the true quality variations in different regions. To improve the approach, adaptive binning techniques could be implemented to dynamically adjust the bin sizes and boundaries based on the content characteristics. Additionally, incorporating machine learning algorithms to learn the optimal binning strategy based on the specific video content could enhance the accuracy of the binned-weighting approach. Furthermore, integrating feedback mechanisms to iteratively refine the binning and weighting process based on the model's performance could lead to more precise quality assessments.

Given the rapid advancements in display technologies, how might the proposed quality assessment framework need to evolve to keep pace with the increasing capabilities of consumer HDR displays

With the rapid advancements in display technologies, the proposed quality assessment framework may need to evolve to keep pace with the increasing capabilities of consumer HDR displays. One key aspect of evolution would be to incorporate dynamic adaptation mechanisms that can adjust the quality assessment criteria based on the specific capabilities of the display device. This could involve real-time monitoring of display characteristics such as peak luminance, color gamut, and contrast ratio, and dynamically optimizing the quality assessment metrics to align with the display's capabilities. Additionally, the framework may need to consider emerging technologies such as HDR with dynamic metadata (e.g., Dolby Vision) and adapt the quality assessment models to account for these advanced features. Continuous research and development to stay abreast of the evolving display technologies and user preferences will be essential to ensure the effectiveness and relevance of the quality assessment framework in the future.