Building Reasonable Inference for Vision-Language Models in Blind Image Quality Assessment

Abstract

This paper presents a novel approach for Blind Image Quality Assessment (BIQA) by leveraging Vision-Language Models (VLMs) to build reasonable inferences about image quality. The study aims to overcome limitations of traditional BIQA methods by utilizing the advanced semantic understanding capabilities of VLMs. It explores methodologies to effectively adapt and prompt VLMs for accurate, human-aligned image quality prediction, demonstrating improved performance across diverse datasets.

1. Introduction

Blind Image Quality Assessment (BIQA) remains a challenging task due to the absence of reference images and the subjective nature of human perception. This work addresses the problem by introducing Vision-Language Models (VLMs) as a powerful tool for inferring image quality. It contextualizes the need for robust BIQA solutions and highlights the potential of VLMs to bridge the gap between image features and perceptual quality. Models used: Specific models are not listed in the provided article content.

2. Related Work

Prior research in BIQA includes various handcrafted feature-based and deep learning methods, often struggling with generalization across diverse image distortions. Recent advancements in Vision-Language Models have enabled sophisticated image understanding and reasoning, showing promise in related perception tasks. This section would review existing BIQA models and VLM applications, establishing the background for the proposed VLM-based inference approach.

3. Methodology

The methodology involves adapting Vision-Language Models for the BIQA task through specific fine-tuning and prompt engineering strategies. It outlines the architectural modifications and the training paradigm designed to enable VLMs to interpret image features in the context of quality degradation. This section would detail the dataset preparation, the VLM model selection, and the loss functions employed for optimizing quality predictions.

4. Experimental Results

Experimental results would demonstrate the efficacy of the proposed VLM-based BIQA framework by comparing its performance against established state-of-the-art methods. Key metrics such as Spearman Rank-order Correlation Coefficient (SRCC) and Pearson Linear Correlation Coefficient (PLCC) would be used to quantify the model's agreement with human perceptual scores on benchmark datasets. This section would present findings showing improved consistency and robustness in quality assessment, particularly in challenging scenarios. The table of results, along with a 2-4 sentence explanation, is not available in the provided article content.

5. Discussion

The discussion interprets the implications of using Vision-Language Models for BIQA, emphasizing their capability to perform nuanced quality inference through cross-modal understanding. It would delve into the strengths of the VLM approach in handling diverse distortions and its potential to capture perceptual subtleties. Furthermore, this section would suggest future research avenues, including enhancing model interpretability and exploring real-world deployment challenges.