结合周边视觉的轻量级立体图像质量评价方法
Lightweight stereoscopic image quality assessment method combi-ning peripheral vision
王杨 1贾曦然 1隆海燕 1韩力英1
作者信息
- 1. 河北工业大学电子信息工程学院,天津 300401;河北工业大学天津市电子材料与器件重点实验室,天津 300401
- 折叠
摘要
针对立体图像质量预测偏差问题,基于人眼视觉模型,提出一种结合周边视觉信息的轻量级立体图像质量评价方法.首先,构建双目感知模型获取中央凹视觉区与周边视觉区,利用对称式立体信息融合模块(symmetrical stereoscopic information fusion,SSIF)加强视差信息;然后,通过轻量级特征提取模块(lightweight feature extraction,LWFE)获取双目质量感知特征;最后,在全连接层实现主客观立体图像质量评价值的关系映射.引入自适应多损失策略指导模型训练,在LIVE 3D与Waterloo IVC立体图像库进行性能测试,结果表明,算法具有良好的综合性能,与人类主观质量感知保持较高的一致性.
Abstract
Aiming at the problem of stereoscopic image quality prediction bias,a lightweight stereoscopic image quality assessment method combining peripheral visual information is proposed based on the human eye vision model.First,a binocular perception model is constructed to acquire the central concave visual area and the peripheral visual area,and a symmetrical stereoscopic information fusion(SSIF)module is used to enhance the parallax information.Then,the binocular quality perception features are obtained by the lightweight feature extraction(LWFE)module.Finally,the relationship between the subjective and objective stereoscopic image quality evaluation values maps is realized in the fully connected layer.An adaptive multi-loss strategy is introduced to guide the model training,while the performance tests are conducted in LIVE 3D and the Waterloo IVC stereoscopic image databases.The results show that the proposed algorithm performs well comprehensive and maintains a high level of consistency with human subjective quality perception.
关键词
立体图像质量评价/周边视觉/轻量级卷积神经网络/自适应多损失Key words
stereoscopic image quality assessment/peripheral vision/lightweight convolutional neural networks/adaptive multi-loss引用本文复制引用
出版年
2024
NETL
NSTL
万方数据