Deep Neural Network for Blind Visual Quality Assessment of 4K Content

by   Wei Lu, et al.

The 4K content can deliver a more immersive visual experience to consumers due to the huge improvement of spatial resolution. However, existing blind image quality assessment (BIQA) methods are not suitable for the original and upscaled 4K contents due to the expanded resolution and specific distortions. In this paper, we propose a deep learning-based BIQA model for 4K content, which on one hand can recognize true and pseudo 4K content and on the other hand can evaluate their perceptual visual quality. Considering the characteristic that high spatial resolution can represent more abundant high-frequency information, we first propose a Grey-level Co-occurrence Matrix (GLCM) based texture complexity measure to select three representative image patches from a 4K image, which can reduce the computational complexity and is proven to be very effective for the overall quality prediction through experiments. Then we extract different kinds of visual features from the intermediate layers of the convolutional neural network (CNN) and integrate them into the quality-aware feature representation. Finally, two multilayer perception (MLP) networks are utilized to map the quality-aware features into the class probability and the quality score for each patch respectively. The overall quality index is obtained through the average pooling of patch results. The proposed model is trained through the multi-task learning manner and we introduce an uncertainty principle to balance the losses of the classification and regression tasks. The experimental results show that the proposed model outperforms all compared BIQA metrics on four 4K content quality assessment databases.


page 1

page 2

page 4

page 8

page 9


A Deep Learning based No-reference Quality Assessment Model for UGC Videos

Quality assessment for User Generated Content (UGC) videos plays an impo...

Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency

The intelligent video surveillance system (IVSS) can automatically analy...

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 ...

Deep Optimization model for Screen Content Image Quality Assessment using Neural Networks

In this paper, we propose a novel quadratic optimized model based on the...

A real-time blind quality-of-experience assessment metric for HTTP adaptive streaming

In today's Internet, HTTP Adaptive Streaming (HAS) is the mainstream sta...

GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment

Nowadays, most 3D model quality assessment (3DQA) methods have been aime...

Unsupervised Deep Power Saving and Contrast Enhancement for OLED Displays

Various power saving and contrast enhancement (PSCE) techniques have bee...

Please sign up or login with your details

Forgot password? Click here to reset