Demystifying the Visual Quality Paradox in Multimodal Large Language Models

TL;DR

This paper reveals a paradox where multimodal large language models perform better with degraded or stylistically shifted images, challenging assumptions about visual quality and proposing a lightweight tuning method to adapt inputs for improved understanding.

Contribution

It systematically studies the impact of visual quality on MLLMs, uncovers the paradoxical effects, and introduces VQ-TTT, a novel, efficient adaptation method to optimize input images for better model performance.

Findings

Degraded or stylized images can improve MLLM performance.

Off-the-shelf restoration does not resolve the quality paradox.

VQ-TTT enhances accuracy without external data or extensive training.

Abstract

Recent Multimodal Large Language Models (MLLMs) excel on benchmark vision-language tasks, yet little is known about how input visual quality shapes their responses. Does higher perceptual quality of images already translate to better MLLM understanding? We conduct the first systematic study spanning leading MLLMs and a suite of vision-language benchmarks, applying controlled degradations and stylistic shifts to each image. Surprisingly, we uncover a visual-quality paradox: model, task, and even individual-instance performance can improve when images deviate from human-perceived fidelity. Off-the-shelf restoration pipelines fail to reconcile these idiosyncratic preferences. To close the gap, we introduce Visual-Quality Test-Time Tuning (VQ-TTT)-a lightweight adaptation module that: (1) inserts a learnable, low-rank kernel before the frozen vision encoder to modulate frequency content; and (2) fine-tunes only shallow vision-encoder layers via LoRA. VQ-TTT dynamically adjusts each input image in a single forward pass, aligning it with task-specific model preferences. Across the evaluated MLLMs and all datasets, VQ-TTT lifts significant average accuracy, with no external models, cached features, or extra training data. These findings redefine ``better'' visual inputs for MLLMs and highlight the need for adaptive, rather than universally ``clean'', imagery, in the new era of AI being the main data customer.