Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images

by   Zicheng Zhang, et al.

Computer graphics images (CGIs) are artificially generated by means of computer programs and are widely perceived under various scenarios, such as games, streaming media, etc. In practical, the quality of CGIs consistently suffers from poor rendering during the production and inevitable compression artifacts during the transmission of multimedia applications. However, few works have been dedicated to dealing with the challenge of computer graphics images quality assessment (CGIQA). Most image quality assessment (IQA) metrics are developed for natural scene images (NSIs) and validated on the databases consisting of NSIs with synthetic distortions, which are not suitable for in-the-wild CGIs. To bridge the gap between evaluating the quality of NSIs and CGIs, we construct a large-scale in-the-wild CGIQA database consisting of 6,000 CGIs (CGIQA-6k) and carry out the subjective experiment in a well-controlled laboratory environment to obtain the accurate perceptual ratings of the CGIs. Then, we propose an effective deep learning-based no-reference (NR) IQA model by utilizing multi-stage feature fusion strategy and multi-stage channel attention mechanism. The major motivation of the proposed model is to make full use of inter-channel information from low-level to high-level since CGIs have apparent patterns as well as rich interactive semantic content. Experimental results show that the proposed method outperforms all other state-of-the-art NR IQA methods on the constructed CGIQA-6k database and other CGIQA-related databases. The database along with the code will be released to facilitate further research.


page 1

page 2

page 3

page 4

page 5

page 9


Subjective Quality Assessment for Images Generated by Computer Graphics

With the development of rendering techniques, computer graphics generate...

Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training

Image quality assessment (IQA) is very important for both end-users and ...

Perceptual Quality Assessment for Digital Human Heads

Digital humans are attracting more and more research interest during the...

KonIQ-10k: Towards an ecologically valid and large-scale IQA database

The main challenge in applying state-of-the-art deep learning methods to...

A Perceptual Quality Assessment Exploration for AIGC Images

AI Generated Content (AIGC) has gained widespread attention with the inc...

KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment

Deep learning methods for image quality assessment (IQA) are limited due...

Full Reference Screen Content Image Quality Assessment by Fusing Multi-level Structure Similarity

The screen content images (SCIs) usually comprise various content types ...

Please sign up or login with your details

Forgot password? Click here to reset