TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment (2308.03060v1)

Abstract: Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. Inspired by the characteristics of the human visual system, existing methods typically use a combination of global and local representations (\ie, multi-scale features) to achieve superior performance. However, most of them adopt simple linear fusion of multi-scale features, and neglect their possibly complex relationship and interaction. In contrast, humans typically first form a global impression to locate important regions and then focus on local details in those regions. We therefore propose a top-down approach that uses high-level semantics to guide the IQA network to focus on semantically important local distortion regions, named as \emph{TOPIQ}. Our approach to IQA involves the design of a heuristic coarse-to-fine network (CFANet) that leverages multi-scale features and progressively propagates multi-level semantic information to low-level representations in a top-down manner. A key component of our approach is the proposed cross-scale attention mechanism, which calculates attention maps for lower level features guided by higher level features. This mechanism emphasizes active semantic regions for low-level distortions, thereby improving performance. CFANet can be used for both Full-Reference (FR) and No-Reference (NR) IQA. We use ResNet50 as its backbone and demonstrate that CFANet achieves better or competitive performance on most public FR and NR benchmarks compared with state-of-the-art methods based on vision transformers, while being much more efficient (with only ${\sim}13\%$ FLOPS of the current best FR method). Codes are released at \url{https://github.com/chaofengc/IQA-PyTorch}.

• The paper introduces CFANet, a top-down architecture that leverages high-level semantics to guide low-level distortion detection in image quality assessment.
• It employs Cross-Scale Attention and Gated Local Pooling to efficiently filter redundant features and focus on semantically important areas.
• Experiments across multiple datasets demonstrate superior generalization and efficiency, aligning closely with human visual judgment.

A Top-Down Approach to Image Quality Assessment: An Analysis of TOPIQ

The paper "TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment" presents a novel methodology in the field of computer vision, centered around Image Quality Assessment (IQA). Recognizing the inadequacies of current methodologies that primarily employ a simple linear fusion of multi-scale features, the authors propose a top-down approach leveraging high-level semantics to enhance the precision of IQA systems.

Methodology

The paper introduces a heuristic architecture, CFANet (Coarse-to-Fine Attention Network), which employs a top-down strategy that mimics the human visual system. CFANet is built on the premise that semantic information can effectively guide distortion perception, a concept overlooked in existing bottom-up and parallel approaches.

Key components of CFANet include:

• Cross-Scale Attention (CSA): This framework uses higher-level semantic features as guides to enhance lower-level distortion perception. The CSA mechanism relies on an attention-based formulation to effectively prioritize semantically important regions.
• Gated Local Pooling (GLP): To maintain efficiency, GLP reduces low-level features in size, thereby lowering computational costs while filtering out redundant information.

The architecture is designed to utilize the ResNet50 backbone, achieving efficient processing with approximately 13% of the floating-point operations compared to the leading alternatives, notably transformers-based methods.

Experimental Results

The experimental evaluations conducted across multiple public FR and NR IQA datasets, such as LIVE, CSIQ, TID2013, PieAPP, and PIPAL, demonstrate that CFANet surpasses or matches the performance of current state-of-the-art models. The paper highlights CFANet's superior generalization capabilities through cross-dataset experiments, exemplifying its robustness and adaptability to diverse image distortions.

Key findings include:

• Excellent intra-dataset performance with high PLCC and SRCC scores.
• Remarkable cross-dataset generalization, with CFANet outperforming larger transformer-based models like AHIQ in computational efficiency and PLCC.
• Demonstrated capacity to replicate human judgment in complex scenarios, closely aligning with human visual preferences.

Implications and Future Directions

The introduction of a top-down paradigm can influence the design of future IQA systems, pushing a transition from traditional feature extraction paradigms to ones that incorporate semantic considerations upfront. The approach holds promise for enhancing perceptual quality metrics in applications like image restoration, compression, and enhancement.

While the paper primarily utilizes ResNet50, promising improvements are observed when using stronger backbones, such as the Swin transformer, particularly in NR tasks. Future research could probe into further integrating advanced transformer architectures and exploring alternative attention mechanisms to enhance semantic transmission and distortion focus.

In summary, the TOPIQ framework introduces a paradigm shift in IQA, leveraging high-level semantics for improved perceptual accuracy and computational efficiency. This approach is poised to influence future developments in AI-driven visual quality assessment, aligning technological capabilities more closely with human visual perception.