A Multibranch Network With Multilayer Feature Fusion for No-Reference Image Quality Assessment
Wenqing Zhao, Mengwei Li, Lijiao Xu, Yue Sun, Zhenbing Zhao, Yongjie Zhai · IEEE Transactions on Instrumentation and Measurement · 2024
With the widespread application of digital images in various domains, the accurate measurement of image quality has become particularly crucial. This paper introduces a novel multi-branch multi-layer feature fusion network (MFFNet) to address the inadequate expression of multi-scale and semantic features and local visual feature consideration in existing no-reference image quality assessment algorithms. MFFNet comprises a primary and a sub-branch. Through convolutional neural network feature extraction, the main branch uses a multi-scale feature enhancement (MSFE) module to capture fine-grained features at each layer, thus significantly enhancing its capability to represent local features. It subsequently merges these distinct-scale features through the multi-layer feature fusion (MLFF) module to improve MFFNet performance. Recognizing human attention to the local image area during image quality evaluation, the sub-branch acquires local visual information using a classical superpixel segmentation model. Finally, the two branches are fused using an element-by-element multiplication operation. Comparative experiments are conducted using four representative datasets—CSIQ, TID2013, LIVEC, and CID2013—demonstrating that the MFFNet method outperforms most advanced techniques, thereby establishing the method’s effectiveness.