Q-Insight: Understanding Image Quality via Visual Reinforcement Learning
Authors: Weiqi Li
Xuanyu Zhang
Shijie Zhao
Yabin ZHANG
Junlin Li
Li zhang
Jian Zhang
Venue: NeurIPS 2025 spotlight ()
Keywords: image quality understanding
multi-modal large language model
reinforcement learning
Primary area: applications
TL;DR: We propose Q-Insight, a reinforcement learning-based IQA approach using limited annotations, achieving superior performance and enhanced visual reasoning capabilities.

Abstract: Image quality assessment (IQA) focuses on the perceptual visual quality of images, playing a crucial role in downstream tasks such as image reconstruction, compression, and generation. The rapid advancement of multi-modal large language models (MLLMs) has significantly broadened the scope of IQA, moving toward comprehensive image quality understanding that incorporates content analysis, degradation perception, and comparison reasoning beyond mere numerical scoring. Previous MLLM-based methods typically either generate numerical scores lacking interpretability or heavily rely on supervised fine-tuning (SFT) using large-scale annotated datasets to provide descriptive assessments, limiting their flexibility and applicability. In this paper, we propose Q-Insight, a reinforcement learning-based model built upon group relative policy optimization (GRPO), which demonstrates strong visual reasoning capability for image quality understanding while requiring only a limited amount of rating scores and degradation labels. By jointly optimizing score regression and degradation perception tasks with carefully designed reward functions, our approach effectively exploits their mutual benefits for enhanced performance. Extensive experiments demonstrate that Q-Insight substantially outperforms existing state-of-the-art methods on both score regression and degradation perception tasks, while exhibiting impressive zero-shot generalization and superior comparison reasoning capability. The code and models are available at https://github.com/bytedance/Q-Insight.

Q-Insight: Understanding Image Quality via Visual Reinforcement Learning
Weiqi Li', Xuanyu Zhang', Shijie Zhao 24:3 Yabin Zhang' Junlin Li,, Li Zhang" Jian Zhang" School of Electronic and Computer Engineering, Peking University ByteDance Inc.
Abstract
Image quality assessment (IQA) focuses the perceptual visual quality of images, playing a crucial role in downstream tasks such as image reconstruction, com- pression, and generation. The rapid advancement of multi-modal large language models (MLLMs) has significantly broadened the scope of IQA, moving toward comprehensive image quality understanding that incorporates content analysis, degradation perception, and comparison reasoning beyond mere numerical scoring. Previous MLLM- methods typically either generate numerical scores lacking interpretability or heavily rely supervised fine-tuning (SFT) using large-scale annotated datasets to provide descriptive assessments, limiting their flexibility and applicability. In this paper, we propose Q-Insight, reinforcement learning-based model built upon group relative policy optimization (GRPO), which demonstrates strong visual reasoning capability for image quality understanding while requir- ing only a limited amount of rating scores and degradation labels. By jointly optimizing score regression and degradation perception tasks with carefully de- signed reward functions, our approach effectively exploits their mutual benefits for enhanced performance. Extensive experiments demonstrate that Q-Insight substan- tially outperforms existing state-of-the methods on both score regression and degradation perception tasks, while exhibiting impressive zero-shot generalization and superior comparison reasoning capability. The code and models are available at httth:: http:::///gihub.... wwt http:/hwwww.mmm/mttdenne//e/e//e Innight
Introduction
Image quality assessment (IQA) is fundamental task in computer vision, critical for optimizing algorithms, enhancing user experiences, and verifying content authenticity across diverse domains,
such image processing 24 I1 53) and Al-generated content (AIGC) [37 g] Traditional IQA
methods rely heavily on hand-crafted metrics, either through reference- based comparisons [50] or statistical measures of natural image properties (64] However, these approaches typically focus on local image characteristics and fail to comprehensively capture global visual quality, limiting their reliability in complex real-world scenarios. More recently, deep learning-basee IQA models [47 [9] have emerged, utilizing neural networks to learn hierarchical image representations. Nevertheless, these methods struggle to face significant challenges in diits--distiiuution (OOD) generalization.
With recent advances in multi-modal large language models (MLLMs) 23
1 48 researchers have begun to leverage these models' extensive world knowledge and perceptual abilities enhance IQA performance and broaden its applicability [53 60 59 58 52]. Existing MLLM-based IQA methods generally fall into two categories: score-based methods, such Q-Align (53) and DeQA- Score [58], and description based methods, exemplified by DepictQA [60] and DepictQA- Wild [59] Score-based methods transform discrete tokens into continuous quality scores, thereby improving
Project Lead. Corresponding authors, zhaoshijie.05266 berrse@yyyddannccoo zhangjian.sze edu.edu.cn.
"This work was supported in part by National Natural Science Foundation of China (No. 62372016).
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure I: PLCC comparisons between our proposed Q-Insight and existing IQA metries (left) and three example applications of our Q-Insight (right) are presented. Q-Insight demonstrates significantly improved performance compared to existing methods such as DeQA-Score (58]. especially on out- of-domain datasets (e.g., CSIQ (20). Additionally, Q- Q-Insight effectively supports quality score regression, image degradation perception, and zero-shot image comparison reasoning tasks.
adaptability but typically sacrificing interpretability and neglecting MLLMs' intrinsic reasoning and descriptive capabilities. Meanwhile, simply regressing quality score may not be meaningful in certain scenarios, as image quality scores are subjective, inherently biased, and lack uniform standards across different datasets and content types. For example, when evaluating AIGC-generated data, unusual visual effects and vibrant colors often imply better quality. However, for evaluating super-eesoution results, these same features are often considered too painterly, losing the image's authenticity and fidelity. Conversely, description- based methods produce detailed textual explanations of image degradations and comparative assessments, ensuring interpretability yet heavily depending on extensive textual depiction for supervised fine-tuning. Moreover, these models cannot output precise scores, making them unsuitable when used as loss functions for performing accurate ranking of image quality. Consequently, integrating numerical scoring and descriptive reasoning into a unified, interpretable MLLM-baseed IQA framework remains an essential yet unresolved challenge.
In this paper, we move towards a comprehensive understanding of image quality by addressing tasks such as image description, aesthetic and compositional evaluation, degradation perception, and comparative reasoning across images. Rather than teaching the large model "How to score images", we aim to inspire it "How to reason deeply and formulate insightuul perspectives on image quality metrics during scoring". To this end, we resort to Group Relative Policy Optimization (GRPO) [40], reinforcement learning framework inspired by DeepSeek-RI 1 [1]. GRPO has recently shown to be highly effective in large language models (LLMs). It uses heuristic reward signals to efficiently guide LLMs in uncovering their intrinsic reasoning capabilities, removing extensive reliance annotated reasoning chains or additional value models. Recently, researchers have also successfully adapted GRPO to langua-language tasks, including few-shot object detection, reasoning grounding medical analysis [4]. In the context of image quality understanding, the introduction of GRPO provides at least three distinct advantages: (1) no reliance on massive textual training data, (2) strong generalization to OOD evaluated images, and (3) high diversity in supporting multiple tasks. These benefits align well with our goal of developing a generalized image quality understanding agent.
(25). and
Specifically, we design Q-Insight upon the GRPO framework. In our Q-Insight, we jointly optimize score regression and degradation perception tasks, and carefully design three reward functions: verifiable score reward for the score regression task, and degradation classification and intensity perception rewards for the degradation perception task. Consequently, Q-Insight effectively exhibits robust reasoning performance using only limited Mean Opinion Scores (MOS) and degradation labels. As shown in Fig. our Q-Insight delivers remarkable performance improvements especially on OOD datasets, while demonstrating comprehensive capabilities across multiple quality assessment and reasoning tasks. For example, it can accurately identify cases where slightly blurred background, usually regarded as undesirable, effectively helps to emphasize the primary subject of the image. Our empirical investigation reveals that: (1) training solely with score labels results in poor perception of image detail degradations (e.g., JPEG compression), while jointly training with the degradation perception task significantly enhances the model's 's sensitivity to such degradations, and (2) score regression and degradation perception tasks are mutually beneficial. Extensive experiments across score regression and degradation perception tasks demonstrate that Q-Insight consistently outperforms existing model-based IQA metrics as well as SFT-driven large language models. Moreover, it exhibits impressive zero-shot generalization on unseen tasks, such as image comparison reasoning, highlighting the robustness and versatility of our method. In summary, our contributions are:
2

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O (1) We propose Q-Insight, the first ceasoning-style multi-modal large language model specifically designed for comprehensive image quality understanding. Unlike previous methods that depend heavily on detailed textual descriptions for supervised fine-tuning (SFT), our approach achieves superior understanding capability using only limited mean opinion scores or degradation labels.
U (2) We introduce a unified framework that jointly optimizes image quality rating and degradation perception, revealing mutual benefits across tasks. Within this framework, we develop three special- ized rewards, including verifiable score reward, degradation classification and intensity perception rewards, enabling the GRPO framework to effectively generalize to low-level vision applications.
(3) Extensive experiments across diverse datasets and IQA tasks demonstrate that Q-Insight consistently outperforms existing model-based IQA metrics well SFT-driven large language models. Moreover, it exhibits impressive zero-shot generalization on unseen tasks, such as reference- based image comparison reasoning, highlighting the robustness and versatility of our method.
2 Related Work
Score-based IQA methods include full-reference and no-reference approaches. Full-reference
methods [50] 42 65 assess image quality by comparing distorted images with high-quality references
using traditional metrics (c.g., SSIM [50]) or advanced deep-learning-base
metrics [3| I0, 9 [3]
36) like LPIPS [66]. Non-reference methods evaluate quality without reference images, shifting quality 74 45 49 Recent multi-modal large language model (MLLM)-based
from traditional handcrafted statistics (27 28, 29 30 31 38] to dcep-learning-drriin priors [88 19 methods, such as Q-Align [53 and DeQA- Score (58), leverage MI..Ms' knowledge and perceptual abilities to produce scores. However, they sacrifice the intrinsic descriptive capabilities of MLLMs.
24 33
73
Description-bass IQA methods utilize the foundational knowledge of MLLMs to deliver detailed qualitative assessments and improved interpretability [5] 52
For instance, MILMS through specifically focuses on comparative quality assessments among multiple images. Approaches such as DepictQA [60] and DepicIQA-Wild [59] handle both single-image and paired-image evaluations across full-reference and no-reference scenarios. Q-Ground (5] emphasizes a detailed visual quality analysis through visual grounding. However, these methods are highly dependent extensive textual annotations for supervised tine-tuning, leading to considerable costs in human labor or GPT token consumption.
60)
59
[4
5
70
71
69
Q-Bench [11 and Q-Instruct [52] enhance the low-level perceptual capabilities
specialized datasets and tailored evaluation strategies. Co-Instruct 54
Reinforcement learning (RL) has emerged as effective strategy to enhance the reasoning per-
formance of LLMS through feedback-driven refinement 43 55
57
17
68
Methods like
RLHF 32 and RLAIF 2 employ human Al-generated feedback to refine model behavior. In vision-language tasks, RI. has successfully been employed to align model predictions closely with human preferences and reduce hallucinations 46 61 62 72). Recently, DeepSeck-RI RI-Zero [15 in- troduced group relative policy optimization (GRPO) [40], leveraging rule-based rewards to strengthen reasoning capabilities without supervised fine-tuning. Furthermore, Visual-RFT 25 applied GRPO to visual grounding, and Med-R1 [34] adopted GRPO for medical reasoning tasks. RI-VL (63 extends GRPO through step- wise optimization for multi-modal reasoning. Distinctly, our Q QnInighht the first to integrate RL-based strategies into the foundational visual quality understanding model. jointly trains on multiple tasks and demonstrates mutually beneficial effects among them.
3 Methodology
3.1 Preliminaries
Group Relative Policy Optinization (GRPO) is innovative reinforcement learning paradigm that has been widely used in models such as DeepSeek RI-Zero [15), achieving excellent results. Unlike Proximal Policy Optimization (PPO) [39], which requires an explicit critic model to evaluate the performance of the policy model, GRPO 40) directly computes the advantage by comparing a group of responses sampled from the policy model, greatly reducing the computational burden.
Specifically, given a query q. GRPO samples N distinct responses (2) _022) ..... ('1)) from the old
policy Tow. Then, the method performs the corresponding actions and receives the respective rewards
(or (1) (2) (N..,,()) according to the task-specific rules. By calculating the mean and standard
3

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Reference Model
Output Group of Score Regnession Task
Score Regression Reward
The (i)-th Ressoning Process: This appcors to be digitil stylized
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1o
othervise
KL-Loss
cripp though lightting depth and texture. Eite inaginative composition suggests ortishi rendenig rather than photographic copture.
Degradation Perception Reword
Queitian What is your quthyot thirpennes?
[1 dogaes des
Rag 10 otherwise
sp-ft + 3.5
sceltt 2.57 ... sonti : Scelt + 375
Multi-Task Query
AI
J II deg dok.
Output Group of Degrodation Perception Task The (1)th Reesoning Process: The image chous no visible nosic, and compress artifactss However, roticesble lack of starpness evident
R_.
othernite
citner
slight comero novemest during exposure. This reaults reduced edge contrest and detail darity.
Policy Model
Multi-Task Group Relative Policy Optimization
day?t Mar
degft: blur
dog biur
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Quessionn: Analyce the diatart on imape distrrtis
level
sight
lev!l:: sight
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Figure 2: Overview of the proposed Q-Insight framework. The policy model receives queries from multiple tasks and generates corresponding groups of responses accompanied by explicit reasoning steps. Task-specific reward functions (Rer Ragg and Rlev) are then applied, and the policy model is subsequently optimized jointly using the multi-task group relative policy optimization algorithm.
deviation of the rewards, the relative advantages of each response can be obtained as follows:
(+) (nean((r()) (2) ...,.(M) (1)
A
std((r(1), ,(2). ,(,)))
where (0) represents the normalized relative quality of the i-th answer. Overall, GRPO guides the policy model to prioritize higher-quality answers that receive higher reward values within the group. After obtaining the advantage A GRPO calculates the ratio of the probabilities of each
response under the new policy Thw and the old policy Te... denoted as p ()) To prevent overly large updates the model and stabilize training, GRPO restricts the p to the range [1 6, 6]. To further maintain closeness to the reference distribution Tee a KL divergence penalty weighted by B adopted. Finally, the optimization objective of GRPO can be formulated as follows:
[(0) -io- Teall
{min [p A0t)) clip (p - 6,1 A DKL[ [al]]] (2)
((())((())))
where p²) = TT Te (0 ( / Te (o q). Q denotes the candidate question set, and DxL. denotes the KL regularization. Teet is typically frozen pre-trained MLLM. GRPO effectively integrates consistent policy updates and strong reward signals in a balanced way. To our knowledge, we are the first apply GRPO to image quality understanding tasks, enabling our model to achieve robust reasoning and generalization performance without heavy reliance on extensive annotated data.
3.2 Overview of Q-Insight
The overall framework of Q-Inihhtt is illustrated in Fig. 2 During training, we jointly optimize two tasks: score regression and degradation perception. Specifically, the multi-modal input for each task comprises an image paired with a task-specific question. Given these inputs, the policy model ma generates groups of answers, each accompanied by explicit reasoning steps. Subsequently, each answer evaluated using its corresponding reward function: Rec for score regression, and Raeg and Ricv for degradation perception. After computing rewards for each group of answers, the policy model optimized jointly via the multi-task GRPO algorithm. Additionally, KL-divergence loss is applied to constrain deviations between the policy model ne and the reference model Nee- During inference, the trained Insight generates coherent reasoning processes and outputs precise answers. Further details regarding multi-task GRPO and data construction are provided in Sec. 3.3 and Sec. 3.4
3.3 Musti-Task Group Relative Policy Optimization
As depicted in Fig. 2 for each input data pair, the policy model na generates a group of N responses,
denoted as (o (0) N /i=1- We then evaluate each of these responses using the proposed reward functions

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(Rue,, Raeg. and Riev) and obtain the overall rewards (; Proper reward design is crucial, as informative and carefully constructed rewards directly facilitate Q-Insight's ' ability to effectively learn reasoning and perception patterns, thus ensuring robust performance across multiple tasks. Specifically, Q-Insight employs a general format reward function shared across all tasks, as well as task-specific reward functions tailored to the unique characteristics of each individual task.
Format reward evaluates whether the reasoning steps are properly enclosed within "<think>" and "</think>" tags, and the final answer is correctly enclosed within "<answer>" and "</answer>" tags [15. Additionally, we require that the content inside "<answer>" tags follow a JSON-like format: beginning with "(", ending with "J", and containing no additional "[" or "J" characters internally. This ensures that Q-Insight can consistently parse results across different tasks. The reward
(1)
score fimt is set to 1 if the i-th response fulfills all the above conditions; otherwise, its reward is 0.
Rewards for score regression task. standard way to quantify image quality by using the Mean Opinion Score (MOS). Instead of directly fitting the MILLM predictions to MOS, we use MOS as a general guideline to motivate the model towards deeper reasoning and generating insightful perspectives during the process of evaluating image quality. Inspired by the treatment of mathematical reasoning tasks in DeepSeek- R [.5], we consider the continuous MOS prediction as either correct or incorrect, thereby adopting binary reward to avoid extremely large or small reward values. Denoting the predicted score of the i-th response as ser pred and the ground-truth score as sorgt, we design the verifiable reward for scoring as follows. The reward value Feer for the i-th response is determined by:
scr = if serpie
pred scrg /< c, otherwise 0,
(3)
where is a predefined threshold. In partieular, if is set to 0, the reward simplifies to exact-answer matching. Otherwise, the threshold allows the model's predicted scores to fluctuate within an acceptable range, rather than strictly requiring exact accuracy. As depicted in Fig. 2 the predicted score receives reward of 1 if it lies within the threshold of the ground-trute truth MOS, and 0 otherwise.
Rewards for degradation perception task. We find that training solely with score labels leads to poor perception of detailed image degradations (e.g., JPEG compression). This may be because generic multimodal models are pre-trained primarily to capture high-level semantic information, causing them to ignore subtle low-level distortions. To address this issue, we jointly train the model with a degradation perception task, leveraging easily obtainable degradation labels, thus enhancing the model's sss sensitivity to these image degradations. In this task, the model is required to predict both the distortion class and the corresponding distortion level. Since distortion class and level inherently discrete variables,
similarly design binary rewards for this task. Denoting the (5)
predicted distortion class and level of the i-th response as degpred and lev pred respectively, we define (1)
the degradation classification reward as follows. The reward value rdgg is determined by:
(i)
if deg = deggr. otherwise 0.
deg
This means the predicted distortion class receives a reward of 1 if correct, and 0 otherwise, as illustrated in Fig. 2 Similarly, the intensity perception reward flev is determined by:
(1) Nev
1
if degpned degg: and lev (o2 levg., otherwise 0.
(5)
As depicted in Fig. 2 the predicted distortion level earns reward of only when both the predicted class and the level exactly match the ground truth; otherwise, it receives O.
Overall multi-task reward. Finally, the overall reward of i-th response is calculated as:
= Fimm llacr ser + llog (as (01 Fdgg + +02
(8)
(1) -iee)
(6)
where ser equals if the score regression task is selected (and otherwise), and similarly, deg equals 1 if the degradation perception task is selected (and 0 otherwise). Note that the reasoning process illustrated in Fig. 2 emmerge naturally from the model's internal capability, without relying on external constraints or additional annotated data. After computing rewards for all generated responses (1) (2) ; ...,, ((N),, the policy model is updated following Eqs. 1 and (2) With this
flexible design, Insight can seamlessly switch between tasks and jointly optimize them during
5

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Table 1: PLCC/ SRRCC comparison on the score regression tasks between our Q-Insight and other competitive IQA methods. All methods except handcrafted ones are trained on the KonIQ dataset.
The best and second-best results of each test setting are highlighted in bold red and underlined blue.
Category
Methods
KonIQ
SPAQ
KADID
PIPAL
LiveW
AGIQA
CSIQ
AVG.
NIQE 22
0,533
0.679
0,468
0.195
0.493
0,560
0.718
0.521
(SPL 2012)
10.530
70.664
/0.405
/0.161
/0.449
70.533
/0.628
/0.481
Handcrafted
BRISQUE [2
0.225
0.490
0,429
0.267
0.361
0,541
0.740
0.436
(TIP 2012)
/0.226
/0,406
/0.356
/0.232
/0.313
/0.497
/0.556
/0.369
NIMA [47
0.896
0.838
0.532
0.390
0.814
0.715
0.695
0.697
(TIP 2018)
/0.859
/0.856
/0,535
/0.399
/0.771
70.654
/0.649
/0.675
HyperIQA [4
0.917
0.791
0.506
0.410
0.772
0.702
0.752
0.693
(CVPR 2020)
/0.906
/0,788
/0,468
/0,403
/0.749
/0,640
/0.717
/0.667
DBCNN [6
0.884
0.812
0.497
0.384
0.773
0.730
0.586
0.667
Non-MLLM Deep-leaming
(TCSVT 2020)
/0.875
/0,806
/0.484
/0.381
70.755
/0.641
/0.572
/0.645
MUSIQ 19
0.924
0.868
0.575
0.431
0,789
0.722
0.771
0.726
(ICCV 2021)
/0.929
/0.863
/0.556
/0.431
/0.830
/0.630
/0.710
/0.707
CLIP-IQA+ (49
0.909
0.866
0.653
0.427
0.832
0.736
0.772
0.742
(AAAI 2023)
/0.895
/0.864
/0.654
/0.419
/0.805
/0.685
/0.719
/0.720
ManIQA [56
0.849
0.768
0.499
0.457
0.849
0.723
0.623
0.681
(CVPR 2022)
/0.834
/0.758
/0.465
/0.452
/0.832
70.636
/0.627
/0.658
C2Score 75
0.923
0.867
0.500
0.354
0.786
0.777
0.735
0.706
(NeurIPS 2024)
/0,910
/0,860
/0.453
/0,342
/0.772
/0.671
/0,705
/0.673
Qwen-SFT 0
0.889
0.874
0.668
0.473
0.734
0.813
0.674
0.732
(Arxiv 2025)
/0.866
/0,875
/0.663
/0.442
/0,728
/0,739
/0.650
/0.709
Q-Align [53
0.941
0.886
0.674
0.403
0.853
0.772
0.671
0.705
MLL.M-based
2024)
/0.940
/0.887
/0.684
/0.419
70,860
/0,735
/0.737
/0.752
DeQA [58]
0.953
0.895
0.694
0.472
0.892
0.809
0.787
0.786
(CVPR 2025)
/0.941
/0.896
/0.687
/0.478
/0,879
/0,729
/0.744
/0.765
Q-Insight
0.933
0.907
0.742
0.486
0.893
0.811
0.870
0.806
(Ours) /0.916
/0.905
/0,736
/0.474
/0,865
/0.764
/0.824
/0.783
During inference, the trained policy model can directly perform image quality understanding without requiring additional fine-tuning. Experimental results presented in Tabs. 3 and [4 and Fig. further demonstrate that jointly addressing the score regression and degradation perception tasks substantially improves performance, highlighting the beneficial interactions between these two tasks.
training.
3.4 Data Construction
We construct multi-modal training data to jointly train Q-Inight on the score regression and degra- dation perception tasks. The prompts designed for each task detailed in Tab. A in the appendix. For the score regression task, the input includes a task-specific prompt and the image to be rated, with the Mean Opinion Score (MOS) serving the guideline calculate the corresponding reward. In the degradation perception task, the input consists of a prompt and an image characterized by specific distortion class and severity level. There are five distortion categories: "noise". "blur", "JPEG", "darken", and "null", where "null" indicates no distortion. Each distortion type has five severity levels: "slight", "moderate", "obvious", "serious". and "catastrophic". The distortion class and corresponding severity level constitute the ground-trum labels to calculate the degradation classi- fication and intensity perception rewards. Overall, our carefully-designed GRPO-based framework and multi-task training strategy help Q-Insight achieve robust reasoning and perception capability even from limited annotated labels. More importantly, this flexible approach facilitates effective generalization to various low-level vision applications, as demonstrated by extensive experiments in Sec. 4 clearly highlighting the advantages of our framework in addressing practical vision tasks.
4 Experiments
4.1 Experimental Setup
Datasets and Metrics. For the score regression task, we use diverse IQA datasets across four categories: (a) in-the-wild datasets, including KonIQ [16). SPAQ [II, and LIVE-Wild [2]: (b)

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thinks
The image shows clear view of the neon sign for Amoeba Music, which is well-lit against a dark background. The text is legible, and the colors are vibrant, indicating that the photo has been taken under good lighting conditions. However, there's no detail visible beyond the sign itself, and background not fully coptured. focus seems to be the sign rather than the surrounding area. </think>
User
Ambbbo
music
Q-Insight
What is your overall rating the quality of this picture?
Question:
<answer> rating: 3.70 </onswer>
Ground Truth: 3.63
<think>
The image appears blurry and unfocused, which significant issue for image quality. It's close-up shot dog, but the lack of sharpness and detail makes it difficult to appreciate the subject fully. The background is also out of focus, which is distracting. The lighting seems adequate, but the composition isn't ideal.
User
Q-Insight
Question: What is your overall rating on the quality of this picture?....
</think>
<answer> rating: 2.60 </nnswe>>
Ground Truth: 2.31
Figure 3: Score rating and explanation results of our Q-Insight. Q-Insight is capable of recognizing text, analyzing the lighting and shading conditions of an image, and understanding its composition.
Table 2: Distortion prediction accuracy (Deg, Acc.) and degradation level accuracy (Lev. Acc.) comparison between our Q-Insight and AgenticIR [6].. Our method outperforms AgenticIR across all degradations, especially in Noise and JPEG Compression.
Method
Metrics
Noise
Blur
JPEG
Darken
Null
Average
Deg. Acc.
0.4646
0.8390
0.0135
0.7478
0.9339
0.5998
AgenticIR 76 (ICLR 2025)
Lev. Acc.
0.1858
0.3219
0.0000
0.2611
0.1922
Deg, Acc.
1.0000
0.9756
1.0000
0.9027
0.7603
0.9277
Q-Insight (Ours)
Lev. Aec.
0,5973
0.4438
0.5541
0.3230
0.4796
synthetic distortion datasets, including KADID 22 and CSIQ [0]: (c) model-processed distortions, including PIPAL [14); and (d) Al-generated images from AGIQA 21. Following (58), we split KonIQ into training and test sets, with approximately 7000 training images. Mcan Opinion Scores (MOS) across these datasets are normalized into the range [1.5]. The remaining datasets are exclusively used to evaluate the model's dii-t-iiiitiittiion (OOD) generalization capability. For degradation perception task, randomly select 7000 images from DQ-495K [59] that contain single distortion for training, with an additional 1000 images reserved for testing. We adopt the pearson linear correlation coefficient (PLCC) and spearman rank-order correlation coefficient (SRCC) as metrics to evaluate performance on score regression task, following 19 53 58 perception,
For degradation
the accuracy of distortion class and degradation level evaluation metrics.
[ as our base model. In the GRPO algorithm, the generation number N is set to 8, the weight of KL divergence penalty 8 is set to 1 x 10 while the weights a and ag set to 0.25 and 0.75, respectively. The threshold is set to 0.35. We employ Adam [26] as the optimizer, using an initial learning rate of 10 that linearly decays to x 1 during training. The model trained for 10 epochs with a total batch size of 128. Training is completed in approximately one day using 16 NVIDIA A100 GPUs.
Implementation Details. We adopt 2.eenn-25-VL-7B-Bme
4.2 Score Regression
We first evaluate our Q-Insight on the score regression task. We compare Q-Insight with handcrafted methods NIQE 29 and BRISQUE 28]: non-MLLM deep-learning methods including NIMA [7]. MUSIQ
[56]: and recent MLLM- based methods such as C2Score 75 Q-Align [33. DeQA-Score [38 and supervised fine-tuned Qwen [. For fair
19 CLIP-IQA+
49 and ManIQA
comparison, all methods (except handcrafted ones) are trained on the KonIQ dataset, and all MILM- based methods utilize approximately 7B parameters. The comparison results in terms of PLCC and SRCC between Insigtt and other IQA methods are presented Tab. [ Compared with the state-of- Mlate-of-the-art the method DeQA-Score, our Insight performs slightly worse on the in-domain KonIQ
7

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Table 3: Ablation study on the score regression task between multi-task and single-task training. Q-Insight with joint-training significantly outperforms score-only training on PLCC/ / SRCC metrics.
Method
KonlQ
SPAQ
KADID
PIPAL.
LiveW
AGIQA
CSIQ
AVG.
0.918
0.903
0.702
0.458
0.870
0.816
0.685
0.765
Ours (Score-Only)
70.895
70,899
70.702
70.435
70.839
/0.766
70.640
/0,739
0.933
0.907
0.742
0.486
0.893
0.811
0.870
0.806
Ours (Joint-Training)
/0.916
/0.905
/0.736
/0.474
/0.865
/0.764
/0.824
/0.783
Table 4: Ablation study on the degradation perception task between multi-task and single-task training. Jointly training with score regression improves the accuracy of degradation perception.
Method
Metries
Noise
Blur
JPEG
Darken
Null
Average
Deg. Acc.
0.9867
0.9268
0.9685
0.8805
0.5702
0.8960
Ours (Degradation-Only)
Lev. Acc.
0,4343
0.3951
0.3108
0.2567
0,3492
Deg. Acc. 1.0000
0.9756
1.0000
0.9027
0.7603
0.9277
Ours Joint-Training
Lev. Acc.
0.5973
0.4438
0.5541
0.3230
0.4796
Question: What's your overall rating on the quality of this picture? The rating should a float between and 5, rounded to two décimal places, with 1 representing poor quality and 5 representing excellent quality.
D w/o Joint Training
D Joint Training
<think>
The image has good lighting and vibrant colors, which contribute to its overall appeal, However, there noticeable noise present, which can detract from the image quality. The horse and rider appear slightly blurry, indicating motion or a lower resolution. Despite these issues, the image still maintains certain level of clority and detail, especially the background. Therefore, would rate the image quality as moderate. </think>
The image appears to be digital illustration rather than photogroph, which can affect its quality. has somewhat pixelated appearance, suggesting might not be high resolution. The colors are vibrant but the details are not sharp. The composition interesting, capturing on action scene well, but the lock of detail and clarity brings down the overall quality.
</think>
<onswer> rating: 3.50 </answer>
<onswer>
rating:: 2.70 </answer>
Figure 4: Subjective ablation comparison between joint multi-task training and w/o joint training on the explanation of image scoring. With joint training, our method can better perceive degradation cues in images (such as pixelated appearance), thereby improving the accuracy of quality assessment.
dataset. However, on -uttof-distributio (OOD) datasets, Q-Insight consistently outperforms all baseline methods across nearly all benchmarks. achieving approximately 0.02 improvements in both PLCC and SRCC. This demonstrates the effectiveness and strong generalization capability of approach. Fig. Biglutillssrrttes two cases showing the reasoning capability in the score regression task. Specifically, our method goes beyond merely outputting numerical scores and provides detailed, structured reasoning. In the first case (top of Fig. 3). Q-Insight correctly identifies and analyzes textual information displayed on neon sign, thoroughly examining details such as lighting conditions. second case (bottom of Fig. 3). Q-Ingihht demonstrates its strength in interpreting image composition aspects, such as the arrangement of visual elements and the primary focal point of the image. These examples further illustrate how Q-Insight advances beyond score regression task, offering valuable insights into image quality by examining various perceptual factors from multiple perspectives, ultimately contributing to a comprehensive understanding of image quality.
the
4.3 Distortion Perception
We further evaluate Q-Insight on the distortion perception task, comparing it with AgenticIR [76). which fine-tunes an MLLM to perform a similar distortion prediction function. The comparative results are presented in Tab. 2 Notably, AgenticIR requires sequential queries for each possible distortion type, whereas Q- Insight identifies distortion types using only single query. Q-Inightt consistently outperforms AgentieIR across nearly all distortion categories, resulting significantly higher average accuracy. However, for the "null" category (no distortion), performance is slightly
8

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Table 5: Ablation study on the threshold for the score regression task. Q-Insight demonstrates robust and stable performance, indicating does not require careful tuning of the threshold
Method
KonIQ
SPAQ
KADID
PIPAL
LiveW
AGIQA
CSIQ
0.15
0,918/0.900
0.903/0.903 903
0.749/0.744
0.443/0.441
0.876/0.847
0.835/0.764
0.857/0.793
0.25
0.929/0.911
0.902/0.902
0.750/0.752
0.472/0.471
0.884/0.855
0.823/0.765
0.889/0.844
0.30
0.932/0.90 912
0.906/0,900
0,742/0.738
0.471/0.460
0.887/0.88 856
0.828/0.773
0.868/0.88
0.40
0,9300.912
0,904/0.901
0.721/0.723
0.446/0.428
0.880/0.85 853
0,819/0.769
0.873/08831
0.50
0.928/0.90 906
0.906/0.902
0.750/0.746
0.475/0.472
0.885/0.854 8554
0.821/0.768
0.894/0.88 850
0.35 (Ours)
0.933/0.916
0,907/0.905
0.742/0.736
0.486/0.474
0.893/0.865
0.811/0.764
0.870/08224
Question: Assess the visual quality of both the Image and the Image B, explain and justify which one is better considering composition and degradation, Your should be Image Image B'.
User
Image
Image
The images depict a seal with a snowy background. In Image A, the contrast slightly enhanced, making the seal's 'ss fur and features more pronounced. This slight increase in contrast marginally improves the visual oppeal by making the image pop more without significantly altering the content. Image B is slightly brightened, which can wash out details and make the image oppear less natural. Comporing the two, Image A retains more of the original content's 's detail and depth, making it the better-quality image
DepictQA
between the two.
Ground Truth: Image
<think>
Both images depict close-up view of what oppears to be fur, possibly from animal. Image A has noticeable blur and lacks sharpness, making it difficult to discern fine details. The composition seems to focus on the texture of the fur, but the lock of clarity detracts from its aesthetic oppeal.
Q-Insight
Image B, on the other hand, shows clearer depiction of the fur's texture and detail. It has less blur and more defined edges, suggesting that it has been taken under better lighting conditions or with higher resolution camero. The composition is similar to Image A, focusing on the same subject matter. < //hink
<onswer>
Image B </answer>
Ground
Truth: Image
Figure 5: Image comparison reasoning results of our Q-Insight and DepictQA 660] Q-Insight outperforms DepictQA in comprehensive content understanding and accurate degradation perception.
lower than AgentielR, possibly because the reasoning mechanism of Q-Insight more sensitive to the presence of potential distortions. Overall, these results highlight Q- Q-Insight's 's ability to efficiently and accurately perceive image distortions through a concise and unified reasoning framework.
4.4 Ablation Studies
Effect of multi-task training. To validate the effectiveness of multi-task training, we compare our jointly trained Q-Insight model with two single-task variants, each trained independently on single task. The comparison results are presented in Tabs. 3 and 4 As shown in Tab, 3 the jointly trained Q-Iigttt significantly outperforms the score-only variant on nearly all datasets, especially on datasets involving synthetic distortions (KADID (22]. CSIQ (20) and those containing model-generatee distortions (PIPAL 14). This demonstrates that incorporating the degradation perception task can effectively enhance performance in the score regression task. Fig. 4 further presents the benefits of multi-task training, showing that QnIighht can precisely identify detailed degradations such as pixel-level artifacts, thereby improving overall accuracy in quality assessment. Similarly, Tab. E indicates that in the degradation perception task, our jointly trained model consistently surpasses the degradation-only variant across all distortion types. This suggests that the score regression task also positively contributes to degradation perception capabilities. These experimental results verify the mutual benefit and effectiveness of the proposed multi-task training strategy. Moreover, our findings clearly show that the visual quality understanding potential of MLLMs can be significantly improved through carefully designed training tasks and learning objectives.
Ablation on the score threshold e. Introducing the threshold allows the model's predictions to vary within an acceptable margin. Tab. reports the ablation results for different choices of e. Q-Insight consistently achieves robust and stable performance across various threshold values, demonstrating that its effectiveness does not depend on careful tuning of e.

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Table 6: Accuracy and PLCC/ SRCC results of the reference- comparison task on the SRbench [6. Reg-Ace and Gen-Ace represent the aceuracy between regression based and generation- based restoration methods, respectively. Q- Q-Insight outperforms score- and description- pirripttoo--beedd methods.
Category
Method
Reg- Acc
Gen-Ace
Overall-Ace
PLCC
SRCC
PSNR
80.07%
41.70%
34.70%
SSIM 50
(TIP 04)
83.00%
45,30%
37.40%
Score-Based
LPIPS 666
(CVPR 18)
82.00%
63.90%
65.80%
A-FINE
6
(CVPR 25)
83.30%
78.90 %
82.40%
DepicIQA 660
(ECCV 24)
73.00%
61.64%
62.96%
0.3457
0.3412
Deseription-Basee
Q-Insight (Zero-Shot)
78.67%
68.64%
75.51 %
0.6385
0.6297
Q-Insight (Trained)
85.67%
77.78%
82.80
0.7627
0.7614
4.5 Image Comparison Reasoning
Our Q-Insight effectively generalizes to zero-shot image comparison reasoning tasks in both reference- based and non-reference- based scenarios, as illustrated in Figs. [5 Specifically, Fig. shows reference-bait based comparison scenario, where the reference image is of lower quality, and Images A and B are outputs generated by two different super-resolution methods. Fig. 5 demonstrates QnIighht's superiority over DepictQA [60], highlighting its enhanced content understanding and precise perception of degradations. These examples illustrate Q-Insight's robust generalization ability, largely enabled by its RL-based framework and multi-task training strategy.
Furthermore, the comparison reasoning performance can be further boosted by training on a small number of labeled comparison pairs using reinforcement learning, Specifically, we randomly sample 5k data pairs from the DifiIQA (6] dataset, where each pair is labeled only with comparison results, without any textual descriptions. Results are shown in Tab. 6 Our Q-Insight consistently surpasses all score-based and description- based methods in terms of overall accuracy and PLCC/SRCC metrics, demonstrating its promising applicability to various image enhancement tasks. Notably, even the zero-shot version of Iniigtt substantially outperforms DepictQA [60,, despite the latter relying on large-scale textual datasets. Additionally, A-Fine (6 utilizes more than 200k data pairs collected from four different datasets, combined with a complex three-stage training pipeline, thus requiring over 40 times more data and considerable effort to develop optimal training strategies. In contrast, Q-Insight achieves superior performance through relatively straightforward yet highly effective visual reinforcement learning. Further details and additional results are provided in Sec. B of the Appendix.
5 Conclusion
In this paper, we introduce Q-Insight, a novel GRPO-based model for comprehensive image quality understanding. It jointly optimizes score regression and degradation perception tasks using only a limited amount of labeled data. Unlike traditional methods that rely on extensive textual annotations or purely numerical scoring, our framework combines numerical accuracy with interpretative reasoning, significantly improving the perceptual analysis capabilities of image quality models. Extensive experiments show that Q-Inihhtt consistently outperforms existing state-of-the-at methods across various datasets and tasks, demonstrating impressive zero-shot generalization and superior comparison reasoning capability. Looking ahead, Insight can extend its capabilities to wide range of tasks, such as image aesthetic assessments, and serve as a powerful discriminative signal to improve image enhancement models. As unified model for scoring, perception, comparison, and reasoning, Q- Insight can act as a central hub, coordinating image reconstruction tools and providing valuable insights into the enhancement process. This integrated and automated system has the potential to revolutionize image quality understanding and enhancement, providing a unified solution that can transform how image quality is evaluated, improved, and applied across various fields.
Limitations. While achieving promising performance, Q-Insight focuses primarily on natural images. Extending to Al-generated images and videos remains essential and is reserved for future exploration. Besides, using a fixed threshold and discrete distortion levels is not the most elegant solution and may allow for more principled approaches. These issues warrant further exploration.
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A Designed Prompts
The prompts designed for each task detailed in Tab. A Specifically, general system prompt is shared across tasks, which encourages the model to explicitly output its reasoning process and provide structured responses. This general prompt supplemented by task-specific prompts tailored for score regression and degradation perception, respectively. For the score regression task, the input includes a task-specific prompt and the image to be rated, with the Mean Opinion Score (MOS) serving as the ground-truth. In the degradation perception task, the input consists of a prompt and an image characterized by a specific distortion class and severity level. We define five distortion categories: "noise", "blur", "JPEG", "darken", and "null", where "null" indicates distortion. Each distortion type has five severity levels: "slight", "moderate", "obvious", "serious", and "catastrophic". The distortion class and corresponding severity level constitute the ground-truth labels. Additionally, for the comparative reasoning scenario, the inputs include a prompt, two images to compared, and an optional reference image.
Table A: Prompts for Different Tasks. The system prompt is shared across all tasks, while task- specific prompts are additionally designed for each individual task.
System Prompt: A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here <tthin>><answe>> answer here </answer>.
Prompt for Score Regression Task: What is your overall rating the quality of this picture? The rating should be float between and 5, rounded to two decimal places, with representing very poor quality and 5 representing excellent quality. Return the final answer in JSON format with the following keys: "rating": The score.
Prompt for Degradation Perception Task: Analyze the given image and determine if it contains
any of the following distortions: "noise", "compression", "blur", or "darken". If a distortion is present, classify its severity "slight", "moderate", "obvious", "serious", or "catastrophic" Return the result in JSON format with the following keys: "distortion_ ccatiionnclsss. The detected distortion (or "null" if none). and "severity": The severity level (or "null" if none).
Prompt for Non- Mon-Referrence-Bel Based Image Comparison: Given Image A: <image_ A> and <Image_ B>, assess the visual quality of both the Image A and the Image B, explain and justify which one is better considering composition and degradation. Your answer should be "Image A" or "Image B".
Prompt for Reference- Based Comparison Reasoning: Given a low-quality reference image and two enhanced outputs. Reference Image: <ref pref_image, Image A: <image A> and Image B: <image_Bs B>. Decide which enhanced image is superior or if they are comparable. Evaluate based fidelity and consistency with the reference image; overall perceptual quality. Return exactly one of: "Image A", "Image B" or "Similar".
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B Experimental Setups and More Results of Comparison Reasoning
B.1
Datasets
For training, we use the DifiIQA [6] dataset, which contains approximately 180k reference-test image pairs generated by applying diffusion-based enhancement methods to reference images of varying quality. Each reference image is paired with multiple test images, and human annotators provide preference labels through triplet-based comparisons, resulting roughly 180k comparison pairs. Notably, A-Fine [6] aggregates datasets from DifiIQA, TID2013 [35]. KADID [22), and PIPAL [14] totaling over 200k comparison pairs for training. In contrast, we randomly sample only 5k pairs from DiffIQA for our training.
For evaluation, we adopt SRIQA-Bench (6, benchmark specifically designed to evaluate the generalization ability of IQA models in real-world super-resolution (SR) scenarios. This dataset includes 100 low-resolution (LR) reference images, each enhanced by 10 distinct SR methods encompassing both regression-basee and generative models. Human raters perform exhaustive pairwise comparisons within each image group, with each comparison annotated by least 10 annotators to ensure reliability. Since no ground-truti high-resolution reference images are provided, models must evaluate perceptual quality based solely on the degraded LR images. Thus, SRIQA- Bench poses a challenging scenario to rigorously assess the robustness of full-reference IQA models under imperfect reference conditions. we report Reg-Ace, Gen acc, and overall ACC, which denote pairwise ranking accuracy on regression-basee SR methods, generation- SR methods, and all outputs, respectively. These metrics measure alignment between model predictions and human judgments under varying super-resolution styles and distortion characteristics. We also report the PLCC and SRCC of Description-bere based methods.
B.2 Reward Design and Training Details
In the image comparison task, the model is expected to determine which image is superior or if they are comparable. Denote predicted score of the i-th response as res we design a comparison reward function Reemp as follows. The reward value (i)
comp for the i-th response is determined by:
if res pred = cempp otherwise O.
comp
Finally, the overall reward of i-th response is calculated as:
(6) V comps
(3)
(8)
(2)
where fint is the format reward. For training, we employ Adam W [26 as the optimizer, using an initial learning rate of x 10 that linearly decays x 10 during training. The model is trained for 8 epochs with a total batch size of 32. Training is completed in approximately 20 hours using 16 NVIDIA 1100 GPUs.
B.3 More Qualitative Results
We provide quantitative results in Tab. in the main paper. Here we present additional qualitative
results of reference- based image comparison reasoning in Figs B and D It can be observed that, despite training on only 5k comparison pairs, Q-Insight surpasses DepiciQA 600 by more effectively recognizing subtle image details and distortions, thereby producing more accurate outcomes.
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Reference Image
(MOS: 1,20)
(MOS:1.00)
The images depict a section of brick building with windows a person stonding the foreground. Image A, there slight blur distortion, reducing the sharpness and person. Image B, slight darkening present, which affects visibility details but retains overall sharpness. Comparing two, Image maintains better detail and texture definition despite being darker, making the image higher quality compared the blurred Image
the brick texture and
0
DepictQA
Ground Truth: Similer
<think>
Both images appear to be blurry and lack detail when compared reference image. There's no discernible difference between Image and Image terms of fidelity and consistency with the reference image as both are equally out of focus and do not show significant details.
Q-Insight
</thinle
sanswer> Similar s/answer>
Ground Truth: Similar
Figure A: Reference-based image comparison reasoning results of our Q-Insight and Depic-
IQA [60] on image "online4" from SRBench (
Reference Image
Image A (MOS: 2,79)
Image (MOS:4.60)
The images depict close-up of ginger root wooden surface. In Image A, there no visible distortions, and the image appears clear and sharp. Image B, however, shows a slight desaturation, which makes the colors appear less vibrant can affect visual appeal, especially natural color is important aspect of the image. Comparing Image A and Image B, Image A has better quality due to its lack of distortion and more accurate color representation making it the superior image terms of visual fidelity.
the ginger's
DepictQA
Ground Truth: Image 8
<think
The comparison of the enhanced images to the original reference image reveals differences clarity, detail, and color saturation. Image A appears slightly more blurred and lacks fine-grained detail, which could indicate loss fidelity. Image however, seems have a clearer sharper appearance, preserving the textures and colors more faithfully than Image </thinlo
Q Q-Insight
<answer> Image B </answer>
Ground
Image
Figure B: Reference-based image comparison reasoning results of our Q-Insight and Depic-
1QA [60] image "online23" from SRBench [
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III
Reference Image
Image A (MOS: 2.95)
Image (MOS:1.27)
The images depict brick building with distinctive tower against blue sky. Image there is slight brightening distortion which makes the image appear bit washed out, reducing contrast and potentially obscuring finer details. Image has slight increase saturation, which enhances colors, making them the quality of Image A and Image B, Image B retains better overall quality. The slight saturation boost in Image enhances visual appeal without introducing any noticeable artifacts or degrading the image integrityy Therefore, Image is the better-quality image between the two.
vivid without significantly altering the image's 's natural appearance. Comparing
DepictQA
Ground Truth: Image
<thinks
The comparison of the two enhanced images to the reference image reveals differences in clarity, detail, color intensity. Image appears have higher level detail clearer textures, while Image seems have slightly more vibrant color palette but less fine-grained detail.
Q-Insight
Both images oppear to be enhancements that aim to improve the original image's quality. Image A has a more balanced enhancement, preserving the structure and details the building without losing natural colors and textures. Image however, seems to have more aggressive enhancement,
potentially causing some loss of fine detail and slight increase in noise.
</thinle
<answer> Image </answer>
Ground Truth: Image
Figure C: Reference-based image comparison reasoning results of our Q-Insight and Depic-
IQA 660] image "realworld33" from SRBench (6.
Reference Image
Image (MOS: 1.86)
Image B (MOS:2.94)
The images depict ned train londscope with trees and grass. In Image A, the slight darkening reduces visibility slightly but retains most details, In Image B, the slight blur affects the sharpness, making details less distinct, especially noticeable the train Comparing the Image maintains detail and overall content integrity despite the darkening, making it the better-quality image compared to Image B, which suffers from loss of detail due to the blur.
foliage.
DepictQA
Ground Truth: Image
<think>
The comparison the two enhanced images to the original reference image reveals differences clarity. detail, and color intensity. Both images appear have undergone subtle distinctions that can be observed. Image A seems slightly more vibrant and detailed than Image B
level enhancement, but there
Q Q-Insight
but also appears to have slight grainy texture that could indicate noise reduction or an increase in providing clearer of the train's details and colors, Both images seem to enhance the original image well, but Image appears to have better balonce between clarity and noise reduction.
image Image has cleaner look smoother edges and less
</thinle
Image s/answer>
Image
Figure D: Reference-based image comparison reasoning results of our Q-Iniggtt and Depic-
IQA [60] image "srtest55" from SRBench (6).
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Justification: The core method development in this research does involve LLMs as any important, original, or non-standard components.
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The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM for what should or should not be described.
26


Reviews:
[{"review_id": "xFZmS0JKCl", "reviewer": "Reviewer_1SVo", "summary": "This paper introduces Q-Insight, a novel approach to No-Reference Image Quality Assessment (NR-IQA) that emphasizes multi-level natural language explanations rather than scalar scores. Unlike traditional IQA methods that predict numeric score, Q-Insight produces tiered rationales for its judgement:\nOverall Quality\nLocal Artifacts\nSpecific Regions or Areas\nThe model uses BLIP2-FlanT5, a vision-language model combining a frozen image encoder with a Flan-T5 text decoder. It is first fine-funed via Supervised Fine-Tuning (SFT) on synthetic instruction-response pairs derived from human-labeled IQA datasets (e.g., KADID-10K, SPAQ). An optional reinforcement learning extension (Q-Insight-RL) is introduced to further improve rationale generation. \nTo evaluate explanation quality, the authors conduct a human study assessing the faithfulness, specificity, and usefulness of the rationales. The work offers more human-aligned justifications in a more accessible and explanatory format.", "questions": "No Scalar Score Prediction: The model does not produce a MOS or DMOS score, which makes direct benchmarking against standard NR-IQA models impossible. Metrics like SRCC/PLCC are not reported. Any comments on this?\nGeneralization Challenges: When evaluated across datasets, the model struggles due to perceptual scale mismatches (e.g., different MOS ranges). These challenges are acknowledged but not addressed. Can you address more on this?\nLimited Role of Reinforcement Learning: RL is not central to the approach. The Q-Insight-RL variant only fine-tunes the text decoder (Flan-T5) using a simple REINFORCE algorithm. The reward is based on agreement with synthetic pairwise preferences, rather than any perceptual fidelity metric (e.g., MOS). Can you address this?\nSynthetic Prompt Limitations: Prompts are derived from heuristic templates based on MOS differences. These may not match real user queries or explanations. More prompt ablation or robustness testing can be beneficial. Any comments on this?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Overall, this paper offers a solid and creative contribution to Image Quality Assessment by shifting the focus toward interpretability through structured natural language explanations. While the use of reinforcement learning is minimal, the core SFT-based approach is well-motivated and complements existing IQA literature. Best suited for venues focused on vision-language modeling or HCI. Would benefit from improved benchmarking and analysis of RL’s impact.\n\nStrengths:\nNovel Approach: Reframes NR-IQA as a language-drive, explainable task\nIntroduces multi-level rationales, structured across perceptual dimensions (global, local, and spatial).\nEmploys a modular and low-cost architecture (BLIP2-FlanT5), avoiding full vision-language retraining.\nIncludes a human evaluation study with clearly defined rubrics.\nUses synthetic instruction tuning that scales across existing datasets.\nThe RL variant (Q-Insight-RL) shows a path toward improving alignment through preference-based training.\n\nOpportunities For Improvement:\nNo Scalar Score Prediction: The model does not produce a MOS or DMOS score, which makes direct benchmarking against standard NR-IQA models impossible. Metrics like SRCC/PLCC are not reported.\nGeneralization Challenges: When evaluated across datasets, the model struggles due to perceptual scale mismatches (e.g., different MOS ranges). These challenges are acknowledged but not addressed.\nLimited Role of Reinforcement Learning: RL is not central to the approach. The Q-Insight-RL variant only fine-tunes the text decoder (Flan-T5) using a simple REINFORCE algorithm. The reward is based on agreement with synthetic pairwise preferences, rather than any perceptual fidelity metric (e.g., MOS).\nSynthetic Prompt Limitations: Prompts are derived from heuristic templates based on MOS differences. These may not match real user queries or explanations. More prompt ablation or robustness testing can be beneficial.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your constructive comments! We hope that our responses can address your concerns. If there are still aspects that need further clarification, please feel free to continue the discussion with us!\n  \n> **Weakness & Question #1: No Scalar Score Prediction**\n- We would like to clarify that our model **indeed predicts a Mean Opinion Score (MOS)**, as explicitly presented in **Tab. 1** of the main paper. In this table, we directly compare our Q-Insight model with state-of-the-art NR-IQA methods including MUSIQ, MANIQA, and DeQA-Score. Additionally, we report standard benchmarking metrics such as PLCC and SRCC. Our results clearly demonstrate superior performance, achieving state-of-the-art results across multiple datasets.\n\n> **Weakness & Question #2: Generalization Challenges**\n- Thank you for raising this concern. Following the practice used in DeQA-Score, we normalize the MOS values from different datasets to a unified 1-5 scale during evaluation. As illustrated in **Tab. 1** of the main paper, our Q-Insight demonstrates superior generalization and consistently achieves **state-of-the-art performance across various out-of-distribution (OOD) datasets**.\n\n> **Weakness & Question #3: Limited Role of Reinforcement Learning**\n - We would like to clarify this misunderstanding. In fact, our Q-Insight employs reinforcement learning for **full fine-tuning of the entire model**. We also carefully design three distinct reward functions, one of which **explicitly utilizes MOS-based rewards to ensure perceptual fidelity**, as defined in **Eq. (3)** of the manuscript.\n\n> **Weakness & Question #4: Synthetic Prompt Limitations**\n-   Thank you for the suggestion. We evaluated four semantically-equivalent prompts (listed below) and measured PLCC/SRCC on seven IQA benchmarks.\n| # | Prompt |\n|---|---|\n| 1 | Evaluate the image and give an overall quality rating. |\n| 2 | What is your general assessment of the image’s visual quality? |\n| 3 | Based on visual inspection, how would you score the quality of this photo? |\n| 4 | What is your overall rating on the quality of this picture? (Ours) |\n-   The full PLCC/SRCC results are summarized in the following table.\n| Prompt # / Metric | KONIQ | SPAQ | KADID | PIPAL | LIVE‑Wild | AGIQA | CSIQ | Average |\n|---|---|---|---|---|---|---|---|---|\n| 1 | 0.934 / 0.915 | 0.903 / 0.901 | 0.744 / 0.735 | 0.483 / 0.473 | 0.894 / 0.866 | 0.814 / 0.763 | 0.869 / 0.821 | 0.806 / 0.782 |\n| 2 | 0.933 / 0.916 | 0.905 / 0.902 | 0.743 / 0.736 | 0.485 / 0.473 | 0.891 / 0.864 | 0.815 / 0.762 | 0.871 / 0.823 | 0.806 / 0.782 |\n| 3 | 0.934 / 0.916 | 0.903 / 0.901 | 0.744 / 0.735 | 0.482 / 0.471 | 0.889 / 0.863 | 0.813 / 0.760 | 0.875 / 0.827 | 0.806 / 0.782 |\n| 4 | 0.933 / 0.916 | 0.907 / 0.905 | 0.742 / 0.736 | 0.486 / 0.474 | 0.893 / 0.865 | 0.811 / 0.764 | 0.870 / 0.824 | 0.806 / 0.783 |\n| **Overall PLCC** | 0.934 ± 0.0005 | 0.905 ± 0.0017 | 0.743 ± 0.0008 | 0.484 ± 0.0016 | 0.892 ± 0.0019 | 0.813 ± 0.0015 | 0.871 ± 0.0023 | 0.806 ± 0.0002 |\n| **Overall SRCC** | 0.916 ± 0.0004 | 0.902 ± 0.0016 | 0.736 ± 0.0005 | 0.473 ± 0.0011 | 0.865 ± 0.0011 | 0.762 ± 0.0015 | 0.824 ± 0.0022 | 0.782 ± 0.0006 |\n\n - Across all datasets, the average PLCC varies by only ± 0.0002 and the average SRCC by ± 0.0006, with the largest per-dataset standard deviations being PLCC ≤ 0.0023 and SRCC ≤ 0.0022 (both on CSIQ). Two-tailed paired t-tests further show no significant differences between any prompt pair (p > 0.05), confirming that **Q-Insight’s performance is robust to reasonable wording changes in real-world use**."}, {"review_id": "SdhmyM5Mxa", "reviewer": "Reviewer_1dVu", "summary": "This paper presents Q-Insight, a no-reference image quality assessment model optimized using the DeepSeek-R1 framework. Q-Insight employs a multi-task learning approach that jointly performs score regression and degradation perception, enabling improved generalization in quality prediction compared to existing NR-IQA methods. Additionally, it is trained using a pairwise comparison strategy, leading to superior performance over state-of-the-art methods on the SRBench benchmark.", "questions": "Q1: Does DQ-495K contain synthetic data that overlaps with any of the test datasets (e.g., CSIQ)?\nThe performance improvement on CSIQ after joint training is significant, raising concerns about potential data leakage or distributional similarity between DQ-495K and the test set.\n\nQ2: The ablation studies on different threshold settings are crucial for understanding the robustness and sensitivity of the proposed method. These results should be presented in the main text rather than being limited to the appendix.\n\nQ3: There are some typographical errors in the reference list.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Incorporating a reinforcement learning (RL)-based optimization method into the IQA task is a novel contribution. It alleviates the need for extensive instruction tuning data, thereby reducing reliance on costly human annotations.\n2. Q-Insight demonstrates superior generalization performance compared to both NR-IQA and FR-IQA models, highlighting the effectiveness of the proposed DeepSeek-R1 optimization and multi-task learning framework.\n3. The paper is clearly written, with comprehensive methodological details and well-structured experimental evaluations.\n\nDespite the model’s strong generalization capabilities, several concerns remain:\n\nWeaknesses:\n1. The effectiveness of the proposed multi-task learning framework is constrained in scenarios where degradation labels are unavailable. Constructing IQA datasets is already resource-intensive, and adding degradation perception further increases the annotation burden.\n2. As shown in Table 4, the performance of the baseline model (Qwen2.5-VL-7B) is not reported. Additionally, the results for degradation level prediction are relatively weak compared to degradation classification, raising concerns about the reliability of the degradation-level perception module.\n3. The thresholding mechanism used for decision-making is dataset-specific, which may limit the model’s applicability and performance across diverse IQA datasets.", "was_revised": true, "final_justification": "See comments.", "rebuttal": "Thank you for your constructive comments! We hope that our response will address all of your concerns. All discussions and supplementary analyses will be included in our revised version. If there are any additional comments to be added, please continue the discussion with us.\n\n> **Weakness & Question #1: Cost of Degradation Labels**\n- Thank you for the comment. In our pipeline, **the degradation labels are actually not manually annotated**. They are automatically generated when we apply standard synthetic distortions (noise, blur, JPEG, darkening) to natural images. Compared to collecting MOS scores, which takes a lot of human effort, **getting degradation labels adds almost no extra cost.** Using these easily obtained labels, Q-Insight achieves the improvements reported in **Tabs. 3 and 4** of the main paper.\n\n> **Weakness & Question #2: Missing Baseline and Low Degradation-Level Accuracy in Tab. 4**\n- **Additional results for Qwen-2.5-VL:** In response to your suggestion, we have evaluated Qwen-2.5-VL-7B on the degradation perception task, and provide the results in the following table. We will include this baseline in the revised version.\n\n  | **Method / Degradation** | **Noise** | **Blur** | **Compression** | **Darken** | **Null** | **Average** |\n  |-------------------------|-----------|----------|----------------|------------|----------|-------------|\n  | AgenticIR               | 46.46% / 18.58% | 83.90% / 32.19% | 1.35% / 0   | 74.78% / 26.11% | 93.39% / -    | 59.98% / 19.22%  |\n  | Qwen-2.5-VL             | 90.27% / 24.78% | 79.51% / 24.39% | 4.05% / 0.90% | 40.71% / 4.43%   | 72.64% / -    | 57.43% / 13.63%  |\n  | Ours-Dist-only          | 98.67% / 43.43% | 92.68% / 39.51% | 96.85% / 31.08% | 88.05% / 25.67% | 57.02% / -    | 89.60% / 34.92%  |\n  | Ours                    | 100.00% / 59.73% | 97.56% / 44.38% | 100.00% / 55.41% | 90.27% / 32.30% | 76.03% / -    | 92.77% / 47.96%  |\n\n- **Regarding degradation-level accuracy:** We would like to clarify that degradation category prediction and degradation level prediction are fundamentally different tasks. Category prediction is a classification problem, while level prediction is essentially regression and thus more challenging. Therefore, **directly comparing their \"accuracies\" is not statistically meaningful**. Moreover, as shown in the above table, the accuracy of degradation level prediction in our method is not low. **Q-Insight achieves substantial improvement in comparison to Qwen and AgenticIR**.\n\n> **Weakness & Question #3: About the Thresholding Mechanism**\n- **The thresholding mechanism is not dataset-specific:** During training, the threshold $\\epsilon$ is **fixed** to 0.35 for every experiment. **During testing, no threshold is needed**—Q-Insight outputs the predicted MOS (with reasoning process) for all test sets.\n- **Q-Insight demonstrates excellent cross-dataset generalization:** as shown in **Tab. 1** of the main paper, Q-Insight trained on KonIQ generalizes well to six other benchmarks, without any per-dataset adjustment.\n\n> **Question #1: Potential Overlap Between DQ-495K and CSIQ Data**\n- **No data leakage:** **There is no overlap between DQ-495K and CSIQ**. Specifically, DQ-495K is built from KADIS-700K [1], whose images are web-crawled and were created to enlarge IQA training data beyond sets such as KonIQ and CSIQ. Besides, the KADIS-700K paper itself also evaluates on CSIQ. This further confirms that there is no overlap between the two datasets. \n- **Reason for larger CSIQ gain:** Q-Insight **does not learn to** score degraded images directly. Instead, predicting distortion type and level as joint training tasks improves its sensitivity to low-level artifacts. As shown in **Fig. A** of the appendix, **multi-task training enables the model to detect pixelation and related defects more accurately**. Since CSIQ contains such synthetic distortions, this extra perceptual capability results in a much larger improvement on CSIQ than on other datasets.\n\n> **Question #2: Threshold Ablation Results Only in Appendix**\n- Thank you for your suggestion. We agree and will move the threshold ablation results from the appendix into the main paper in the final version.\n\n> **Question #3: Typographical Errors in References**\n- Thank you for pointing this out. We will correct all reference typos in the final version.\n\n> **References**\n\n[1] DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning, QoMEX 2019"}, {"review_id": "JqBWezplSX", "reviewer": "Reviewer_vavm", "summary": "This paper proposes an image quality assessment method using multi-modal large language models (MLLMs). By leveraging Group Relative Policy Optimization (GRPO), the method enables reasoning using MLLMs based solely on ground-truth labels, without requiring any ground-truth reasoning texts. In particular, it combines a Score Regression Reward with a Degradation Perception Reward to fully exploit the potential of MLLMs. Experiments demonstrate that, compared to non-reasoning methods, the proposed approach achieves significantly better generalization performance, especially on unseen datasets.", "questions": "1. Could the authors please clarify Weakness 1? If my current understanding is incorrect and the existing methods are actually trained on both the KonIQ dataset and DQ-495K, please indicate so. However, if existing methods are trained only on the KonIQ dataset, please clarify how this evaluation can be considered truly fair.\n\n2. Regarding Weakness 2, could the authors elaborate further? For instance, if GRPO is replaced with multi-task supervised fine-tuning in the proposed method, would the strong generalization performance be lost?\n\n3. For Weakness 3, could the authors provide more explanation? Has it been evaluated whether reasonable reasoning is consistently performed across the entire dataset?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1. The idea of applying GRPO to image quality assessment is highly interesting. It reveals a new potential of MLLMs and represents a significant contribution for future researchers. The method is able to illustrate the reasoning process of MLLMs using only ground truth labels, which adds to its practical significance.\n\n2. The proposed reward combines a score regression task and a degradation perception task. This idea is reasonable and contributes significantly to performance improvements.\n\n3. The proposed method shows strong generalization performance, particularly on out-of-distribution (OOD) datasets. This is especially valuable in practical scenarios where test data may differ from the training datasets, making the method more applicable in real-world settings.\n\n**Weaknesses**\n\n1. The authors state, \"For a fair comparison, all methods (except handcrafted ones) are trained on the KonIQ dataset.\" However, the proposed method is trained using both the KonIQ dataset and DQ-495K, which raises concerns about the fairness of the comparison. The \"Ours (Score-Only)\" model in Table 3 is trained only on the KonIQ dataset, and comparisons between this model and existing methods might provide a fairer evaluation. However, in this setting, the \"Ours (Score-Only)\" model underperforms compared to DeQA, which further raises concerns.\n\n2. The paper claims that “the introduction of GRPO provides at least three distinct advantages: … (2) strong generalization to OOD evaluated images.” However, it is questionable whether GRPO itself is responsible for the strong generalization. From Table 3, it appears that the generalization may primarily be due to the joint-training, i.e., the increased training data. The fact that more training data leads to better generalization is not a particularly novel finding.\n\n3. The validity of the model's reasoning is not evaluated. Although Figure 3 presents two examples of reasoning, there is no assessment of whether the reasoning is valid across the entire dataset. If the model happens to produce correct scores based on poor reasoning, it could pose practical issues. DepictQA uses GPT-4 scores and the \"Reasonable rate by human evaluators\" to evaluate the reasoning validity, but they are not employed in this paper.", "was_revised": true, "final_justification": "The authors responded to my concerns in a thoughtful manner, and my concerns were resolved. I raised my rating accordingly.", "rebuttal": "Thank you for your constructive comments! We hope that our responses can address your concerns. If there are still aspects that need further clarification, please feel free to continue the discussion with us!\n\n> **Weakness & Question #1: Fairness of Training Data**\n- Thank you for the kind suggestion. We would like to clarify that the comparison is **fair**. This is because **DQ-495K provides only degradation labels**, which cannot be utilized by existing score-only methods such as DeQA-Score. In contrast, Q-Insight leverages both score regression and degradation perception tasks, allowing them to reinforce each other through the design of our multi-task GRPO training. **The introduction of DQ-495K is not merely for comparison purposes—it enables multi-task learning, which is a core contribution of our method and leads to improved performance across diverse datasets**.\n\n- To further investigate the impact of training data size on performance, we trained two Q-Insight variants: \n  - **#1**: Used 2K images with score labels + 2K images with degradation labels (**total data size is about 57% of other methods**)\n  - **#2**: Used 3.5K images with score labels  + 3.5K images with degradation labels (**total data size is aligned with other methods**) \n- As shown in the following table, **both #1 and #2 Q-Insight variants outperform DeQA-Score**, even under equal or reduced data conditions. This demonstrates that our method provides performance advantages **even with the same or less training data**. \n\n  | Method / Dataset | KONIQ         | SPAQ         | KADID        | PIPAL        | LIVE‑Wild      | AGIQA         | CSIQ          | Average        |\n  |------------------|--------------|--------------|--------------|--------------|----------------|---------------|---------------|----------------|\n  | DeQA-Score       | 0.953 / 0.941| 0.895 / 0.896| 0.694 / 0.687| 0.472 / 0.478| 0.892 / 0.879  | 0.809 / 0.729 | 0.787 / 0.744 | 0.786 / 0.765  |\n  | #1 (2K+2K)       | 0.910 / 0.886| 0.906 / 0.901| 0.721 / 0.719| 0.449 / 0.447| 0.869 / 0.831  | 0.837 / 0.785 | 0.884 / 0.833 | 0.796 / 0.772  |\n  | #2 (3.5K+3.5K)   | 0.918 / 0.893| 0.906 / 0.903| 0.749 / 0.748| 0.492 / 0.475| 0.875 / 0.840  | 0.817 / 0.767 | 0.868 / 0.818 | 0.804 / 0.778  |\n  | Ours             | 0.933 / 0.916| 0.907 / 0.905| 0.742 / 0.736| 0.486 / 0.474| 0.893 / 0.865  | 0.811 / 0.764 | 0.870 / 0.824 | 0.806 / 0.783  |\n\n\n- Beyond scoring accuracy, **Q-Insight offers additional advantages**, such as generating a complete and interpretable reasoning process, supporting degradation perception, and enabling image comparison tasks. These expanded capabilities further demonstrate the strengths of our approach.\n\n> **Weakness & Question #2: Attribution of Generalization**\n- Thank you for your valuable suggestion. According to your advice, we conducted experiments by fine-tuning Qwen-2.5-VL on multi-task data using standard supervised learning instead of GRPO. The results are shown in the table below. We observed that **simply increasing the amount of multi-task training data through supervised fine-tuning only led to minor fluctuations (~0.001) in performance** and did not significantly improve generalization ability. This may be because supervised fine-tuning mainly helps the model memorize knowledge for specific tasks, but does not enable the model to explore or establish connections between different tasks through reasoning. In contrast, Q-Insight trained on multi-task learning shows significant performance gains compared to its variant trained only on the score regression task. Therefore, we believe that **GRPO is responsible for the strong generalization performance observed in our method**, as it encourages the model to reason and leverage information between tasks, rather than just fitting facts from the data.\n\n  | Method / Dataset         | KONIQ         | SPAQ         | KADID        | PIPAL        | LIVE-Wild    | AGIQA         | CSIQ          | Average        |\n  |-------------------------|--------------|--------------|--------------|--------------|--------------|---------------|---------------|----------------|\n  | Qwen-SFT (Score-Only)   | 0.889 / 0.866| 0.874 / 0.875| 0.668 / 0.663| 0.473 / 0.442| 0.734 / 0.728| 0.813 / 0.739 | 0.674 / 0.650 | 0.732 / 0.709  |\n  | Qwen-SFT (Multi Tasks)  | 0.891 / 0.866| 0.880 / 0.878| 0.661 / 0.659| 0.467 / 0.450| 0.760 / 0.739| 0.801 / 0.732 | 0.671 / 0.648 | 0.733 / 0.710  |\n  | Q-Insight (Score-Only)  | 0.918 / 0.895| 0.903 / 0.899| 0.702 / 0.702| 0.458 / 0.435| 0.870 / 0.839| 0.816 / 0.766 | 0.685 / 0.640 | 0.765 / 0.739  |\n  | Q-Insight (Multi-Tasks) | 0.933 / 0.916| 0.907 / 0.905| 0.742 / 0.736| 0.486 / 0.474| 0.893 / 0.865| 0.811 / 0.764 | 0.870 / 0.824 | 0.806 / 0.783  |\n\n> **Weakness & Question #3: Evaluation of Reasoning Validity**\n\nThank you for your suggestion. Following your advice, we conducted two additional experiments to evaluate the validity of the model’s reasoning process. These results demonstrate the stable and superior reasoning performance of Q-Insight across the entire dataset.\n  - We randomly selected about 7,600 samples from the three tasks. For each sample, we provided the original image and the reasoning outputs of both DepictQA and Q-Insight to GPT-4o. GPT-4o then assigned a reasoning score (1-5) based on accuracy and helpfulness, as well as a preference label. The table below presents the score / preference rate for all three tasks. **Q-Insight consistently obtains higher GPT-4 scores and preference rates than DepictQA across all tasks**.\n\n    |                 | Score Regression | Degradation Perception | Image Comparison | Average      |\n    |-----------------|-----------------|-----------------------|------------------|--------------|\n    | DepictQA        | 3.71 / 21.14%   | 3.86 / 12.20%         | 3.89 / 19.31%    | 3.84 / 19.72%|\n    | Q-Insight       | 4.11 / 78.86%   | 4.32 / 87.80%         | 4.35 / 80.69%    | 4.29 / 80.28%|\n\n  - We also performed a user study, randomly sampling 10 cases for each task, with a total of 16 human experts participating. The results, shown in the table below, indicate that **Q-Insight was preferred by a majority of human evaluators**.\n\n    |                 | Score Regression | Degradation Perception | Image Comparison | Average |\n    |-----------------|-----------------|-----------------------|------------------|---------|\n    | DepictQA        | 36.87%          | 31.25%                | 24.37%           | 30.83%  |\n    | Q-Insight       | 63.13%          | 68.75%                | 75.63%           | 69.17%  |"}, {"review_id": "1PazwG9kTo", "reviewer": "Reviewer_51Yp", "summary": "This work mainly addresses the two challenges of current MLLM-based image quality assessment methods: less interpretation of estimated scores and the data-hungry issue. This work applied reinforcement learning-based finetuning on MLLM for image quality understanding. Specifically, three tasks are considered: quality score regression, degradation perception (estimating distortion type and level), and comparison reasoning. The Group Relative Policy Optimization is utilized as the reinforcement learning algorithm to solve the problem. The proposed framework demonstrates a great out-of-distribution generalization performance, and the qualitative results show the proposed framework's potential for a deep understanding of image quality.", "questions": "Corresponding to the five listed major weaknesses, my questions or suggestions are listed as follows:\n\n1) Could you conduct preliminary subjective experiments on comparing the reasoning capability for DepictQA and Q-Insight? If the experiment design and results are satisfactory, I will increase my evaluation score.\n\n2) Could you analyze the possibility of extending the proposed framework to more complicated distortion types? Furthermore, if you could show the potential for this task by extra experimental results, I will increase my evaluation score.\n\n3) Could you explain why smaller thresholds cannot lead to a better score estimation performance?\n\n4) I suggest the authors add one extra experiment to demonstrate the stability of the RL algorithm.\n\n5) I suggest the authors optimize the experiments design for comparing non-MLLM methods and MLLM methods.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n1) This is a high quality work, solid technical contribution, well-written and easy-to-follow paper writing, well-explained concepts, and the experimental results support the claims proposed in the paper.\n\n2) The proposed method has been proven data efficient for achieving competitive results with few data points.\n\t\n3) The experimental results show a good generalization capability of the proposed framework on quality score regression tasks. The qualitative results are carefully presented with rich details.\n\nMajor Weaknesses:\n\t\n1) In the paper, the comparison reasoning capability is demonstrated by several qualitative cases comparing the reasoning processes of DepictQA and Q-Insight, which is not sufficient. A large number of well-organized subjective experiments would be needed for a thorough comparison between these two methods, which is a common technique for evaluation in the field of quality assessment.\n\n2) The current setting on distortion types is too simple, only five distortion types are selected, and one of them is \"null\". From Table 4, we can see the model almost achieves 100% accuracy on the distortion classification task. However, distortions are really complicated in quality assessment research, there are multiple types of distortions and it could be the case that multiple distortions exist in the same picture. I feel MLLM may have the potential to be generalized to more complicated distortion scenarios, using few-shot prompting or the generalization ability of the language model. Overall, I think there is still some space to explore looking into this direction.\n\n3) In Table B, the results show that a smaller threshold cannot lead to a better score estimation performance, which is counterintuitive. Can you explain why?\n\n4) The stability issue of RL has not been explored, especially considering that, in image quality assessment, the dataset is comparatively smaller.\n\n5) In Table 1, the authors compared several non-MLLM deep-learning methods. I have several concerns here: 1) more recent deep learning-based methods should be compared, currently, the most updated method is CLIP-IQA+ (AAAI 2023). 2) I feel like it is not a fair comparison because the non-MLLM deep learning models are trained from scratch and are much smaller. Therefore, the comparison can be multiple dimensional by incorporating: a) methods extension with pretraining; and b) training/testing cost comparison.\n\nMinor weaknesses (could be improved, but not necessary to respond):\n\t\n1) Need to add a citation for \"few-shot object detection\"\n\t\n2) Need to explain how the hyper-parameters from line 234 to line 237 are selected.\n\n3) How the inputs and prompts are organized, especially in no-reference or full-reference settings, should be describepd in the appendix.\n\n4) How model is trained is missing. Since there are two different tasks using different datasets, it is not that straightforward to figure out the training procedure.", "was_revised": true, "final_justification": "The authors have addressed my concerns well, especially regarding the evaluation of the reasoning capability of the proposed framework. I also reviewed the discussion between the authors and other reviewers. It seems like the other reviewers are also satisfied with the rebuttal. Therefore, I decided to change my rating from 3 to 5.", "rebuttal": "Thank you for your constructive comments! We hope that our responses can address your concerns. If there are still aspects that need further clarification, please feel free to continue the discussion with us!\n> **Weakness & Question 1: Experiments on Comparing the Reasoning Capability**\n\n  Thank you for your suggestion. Following your advice, we conducted the following experiments to compare the reasoning capabilities of DepictQA and Q-Insight. These additional experiments provide **objective and subjective** evidence for the stable and superior reasoning performance of Q-Insight over DepictQA across the dataset.\n- We randomly sampled approximately 7,600 cases from all three tasks. For each sample, the original image and the reasoning outputs of both DepictQA and Q-Insight were evaluated by GPT-4o, which provided a reasoning score (1-5) considering both accuracy and helpfulness, as well as a preference label. The results below show that **Q-Insight consistently achieved higher GPT-4o scores and preference rates than DepictQA across all tasks**. \n\n  |              | Score Regression | Degradation Perception | Image Comparison | Average      |\n  |--------------|-----------------|-----------------------|------------------|--------------|\n  | DepictQA     | 3.71 / 21.14%   | 3.86 / 12.20%         | 3.89 / 19.31%    | 3.84 / 19.72%|\n  | Q-Insight    | 4.11 / 78.86%   | 4.32 / 87.80%         | 4.35 / 80.69%    | 4.29 / 80.28%|\n\n- We also carried out a **subjective user study**, randomly selecting 10 cases for each task and involving 16 human experts. As shown in the table below, **the majority of human evaluators preferred Q-Insight’s reasoning results**.\n\n  |              | Score Regression | Degradation Perception | Image Comparison | Average |\n  |--------------|-----------------|-----------------------|------------------|---------|\n  | DepictQA     | 36.87%          | 31.25%                | 24.37%           | 30.83%  |\n  | Q-Insight    | 63.13%          | 68.75%                | 75.63%           | 69.17%  |\n\n> **Weakness & Question 2: Extension to More Complex Distortion Types**\n\nThank you for your valuable suggestion. Based on your advice, we analyzed and validated the potential of Q-Insight to handle more complex distortion scenarios from two perspectives. These findings provide strong evidence of Q-Insight’s potential for generalizing to more complicated and realistic distortion scenarios:\n\n- **Unseen (OOD) Distortion Types:**  We evaluated Q-Insight’s ability to detect distortion types that were not included in the training set (e.g., \"rain\", \"haze\", and \"brighten\"). By directly appending these new distortion types to the prompts, the results (shown in the table below) indicate that Q-Insight still **achieves over 80% accuracy on these unseen distortions**, demonstrating the strong generalization capability of our method as an MLLM.\n\n  |         | Rain   | Haze   | Brighten |\n  |---------|--------|--------|----------|\n  | Q-Insight | 97.00% | 94.50% | 81.50%   |\n\n- **Mixed and Complex Distortions:** We further tested Q-Insight on images containing **three mixed distortions**. Using a **zero-shot** prompting strategy, we added statements indicating that \"multiple types of distortion may exist in the image\" to the prompt. The results show that Q-Insight detected **at least two distortions in approximately 95% of the cases** and detected **all three distortions in about 60% of the cases**.\n\n    |                                  | Identify at least 1 distortion | Identify at least 2 distortions | Identify all 3 distortions |\n    |----------------------------------|-------------------------------|-------------------------------|----------------------------|\n    | Q-Insight                        | 100.00%                       | 94.50%                        | 59.50%                     |\n\n\n> **Weakness & Question 3: Effect of Threshold on Score Estimation**\n- Thank you for your question. The reason why a smaller threshold does not lead to better score estimation performance is related to **the way Q-Insight is trained with Group Relative Policy Optimization (GRPO)**.\n- GRPO relies on the model generating a group of candidate answers, which are then evaluated using a verifiable reward—only outputs that satisfy the set threshold condition receive a reward, guiding the model's learning process. **If the threshold is set too small, the requirement becomes overly strict**. As a result, very few or even none of the model's outputs meet the condition to receive a reward. This leads to sparse or weak reward signals during training, hindering the model’s ability to learn effectively and making it difficult for the model to converge toward optimal score estimation performance.\n- In summary, **overly strict thresholds reduce the opportunities for positive feedback in reinforcement learning**, ultimately hurting the model’s performance.\n\n> **Weakness & Question 4: About RL Stability Evaluation**\n- Thank you for highlighting the importance of RL stability. In our GRPO-based RL framework, **training stability is mainly ensured by the KL-divergence constraint between the policy model being trained and the reference model (Qwen-2.5-VL)**, as described in **Eq. (2)** of the main paper. This KL constraint prevents the trained model from deviating too far from the reference model during the learning process.\n- Following your suggestion, we conducted **additional ablation experiments by varying the value of the KL coefficient** $\\beta$. As shown in the table below, setting $\\beta$ too high results in overly strong constraints, causing the model to stay too close to the original reference and thus degrading performance. Conversely, setting $\\beta$ too low weakens the constraint, which can lead to unstable training or even training collapse. We will include these experimental results and analysis in the revised version to further demonstrate the stability of our RL algorithm.\n\n  | Method / Dataset | KONIQ         | SPAQ         | KADID        | PIPAL        | LIVE‑Wild      | AGIQA         | CSIQ          | Average        |\n  |------------------|--------------|--------------|--------------|--------------|----------------|---------------|---------------|----------------|\n  | $\\beta$ = 0.04   | 0.903 / 0.879| 0.902 / 0.897| 0.740 / 0.734| 0.455 / 0.441| 0.866 / 0.829  | 0.836 / 0.793 | 0.871 / 0.821 | 0.796 / 0.771  |\n  | $\\beta$ = 0.01   | 0.932 / 0.913| 0.908 / 0.904| 0.741 / 0.733| 0.473 / 0.458| 0.892 / 0.862  | 0.814 / 0.769 | 0.867 / 0.818 | 0.804 / 0.780  |\n  | $\\beta$ = 0.001 (Ours) | 0.933 / 0.916| 0.907 / 0.905| 0.742 / 0.736| 0.486 / 0.474| 0.893 / 0.865 | 0.811 / 0.764 | 0.870 / 0.824 | 0.806 / 0.783  |\n  | $\\beta$ = 0.0001 | Training collapsed |              |              |              |                |               |               |                |\n\n> **Weakness & Question 5: Experimental Design for Comparison**\n- Thank you for your valuable suggestions regarding the comparison design. We have added a more **recent and widely adopted method**, LIQE [1], to our comparison. As shown in the following table, Q-Insight consistently outperforms LIQE.\n\n  | Method / Dataset | KONIQ         | SPAQ         | KADID        | LIVE-Wild    | AGIQA         |\n  |------------------|--------------|--------------|--------------|--------------|---------------|\n  | LIQE             | 0.928 / 0.912| 0.833 / 0.846| 0.662 / 0.667| 0.870 / 0.830| 0.708 / 0.772 |\n  | **Ours**         | **0.933 / 0.916** | **0.907 / 0.905** | **0.742 / 0.736** | **0.893 / 0.865** | **0.811 / 0.764** |\n\n- We would like to clarify the role of pre-training in these comparisons. The core motivation of our work is to leverage the prior knowledge and large-scale data inherent in foundation models for downstream tasks, which we believe is itself valuable and represents a key advantage of using MLLMs. **The pre-training cost is not directly included in the downstream task evaluation, as is common practice in the field**. Furthermore, many non-MLLM deep learning models also rely on pre-training. For example, MUSIQ is pre-trained on ImageNet, and CLIP-IQA utilizes a pre-trained CLIP backbone.\n- To make the comparisons as clear as possible, we have already distinguished MLLM-based and non-MLLM-based methods in our tables. Following your suggestion, **we will further organize the results in the final version** to include (1) categorization of methods as pretrained or non-pretrained, and (2) an analysis of training and inference costs. We appreciate your feedback and will incorporate these improvements to provide a more comprehensive comparison.\n\n> **Minor Weaknesses**\n- Thank you for your suggestion. We will add discussion and a citation for \"few-shot object detection\" in the final version.\n- Thank you for pointing this out. We will provide an explanation for the hyper-parameter selection in the final version.\n- Thank you for your comment. We have already described the organization of inputs and prompts in **Sec. 3.4** of the main paper and **Sec. A/Table A** of the appendix.\n- Thank you for your feedback. We have already described the multi-task training procedure in the Methods section, specifically in **lines 144-153 and 194-204** of the main paper.\n\n> **References**\n\n[1] Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective, CVPR 2023."}]

Author rebuttal:

Meta-review: This paper presents Q-Insight, an No-Reference Image Quality Assessment (NR-IQA) model that integrates multi-level natural language explanations and enhances image quality evaluation beyond traditional numeric scores. It uses a vision-language model (BLIP2-FlanT5) and a multi-task learning framework and combines score regression, degradation perception, and reasoning based solely on ground-truth labels. It achieves superior generalization and performance on benchmarks like SRBench. The model also includes an optional reinforcement learning extension (Q-Insight-RL) for improved rationale generation. A human study evaluates the faithfulness, specificity, and usefulness of its explanations, addressing challenges in current MLLM-based methods related to interpretation and data efficiency, and showcasing its strong out-of-distribution generalization capability.

Initial reviewer opinions were somewhat mixed on this paper. Reviewers raised concerns regarding the evaluation methodology, stability of the RL-based approach, and fairness of the comparative performance analysis. Most reviewer concerns were adequately addressed in rebuttal during the author-reviewer discussion phase. Reviewers were otherwise nearly unanimous in their recognition that this paper is sound and clearly written, and that is proposes a novel and effective approach that generalizes significantly better than the current state-of-the-art. The recommendation is thus to accept as a spotlight. Deep RL is a core NeurIPS topic, and this paper proposes a novel application of it to IQA (a core Computer Vision problem).
Decision: Accept (spotlight)