VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank
Authors: Tianhe Wu
Jian Zou
Jie Liang
Lei Zhang
Kede Ma
Venue: NeurIPS 2025 spotlight ()
Keywords: Image Quality Assessment
Reinforcement Learning
Reasoning-induced no-reference IQA model
Primary area: applications
TL;DR: We develop a reasoning-induced NR-IQA model via reinforcement learning to rank.

Abstract: DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing reasoning and generalization capabilities of large language models (LLMs) through reinforcement learning. Nevertheless, the potential of reasoning-induced computation has not been thoroughly explored in the context of image quality assessment (IQA), a task depending critically on visual reasoning. In this paper, we introduce VisualQuality-R1, a reasoning-induced no-reference IQA (NR-IQA) model, and we train it with reinforcement learning to rank, a learning algorithm tailored to the intrinsically relative nature of visual quality. Specifically, for a pair of images, we employ group relative policy optimization to generate multiple quality scores for each image. These estimates are used to compute comparative probabilities of one image having higher quality than the other under the Thurstone model. Rewards for each quality estimate are defined using continuous fidelity measures rather than discretized binary labels. Extensive experiments show that the proposed VisualQuality-R1 consistently outperforms discriminative deep learning-based NR-IQA models as well as a recent reasoning-induced quality regression method. Moreover, VisualQuality-R1 is capable of generating contextually rich, human-aligned quality descriptions, and supports multi-dataset training without requiring perceptual scale realignment. These features make VisualQuality-R1 especially well-suited for reliably measuring progress in a wide range of image processing tasks like super-resolution and image generation.

VisualQuality-Ie Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank
Tianhe Wu Jian Zou', Jie Liang?, Lei Zhang 2,3, Kede Ma
City University of Hong Kong
2PPO Research Institute
The Hong Kong Polytechnic University
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calzhang&comp polyu. edu.hk, hk, kede. www.mmma.mttteeeeeeeee https:/ thub.
edu
KonlQ-10K
1200
0439
3.ME
Please describe the quañiy of this image.
Please describe the quality of this image.
0.57
Q- R-Instruct
Q-Instruct
Clear, and the composition is centered: The main subject, bird the middle, is clearly defired. Therefore, the quality good
Are rich, and the liphting is sufficient. The tenture dctails are not clear. The composition centered, and subject clearly mon middle.
SRICA-Bench Bench
Therefore, the quality poor.
Deak-Scoee
0.0%
VisuaiQuality-Ri
VisualQuality-Ri
0755
of high qualty, featuring shorp depiction of a standing water with its reflection visible below. details beak, and legs are well-defined, indicating good focus and lighting. water's surfoce smooth, creating a clean and symmetrical reflection that enhances the composition. background appears to be natural setting, possibly a wetland morsh, which adds context the scene without distracting from the main subject. The colors are vibrant, with brown contrasting nicely uguinst the blue tones the image well. compused und coptures the exsence of the bird in its natural habitot.
image appeors of quality, narkcoblee issues:
0.96
5395
8
Blurrnees:: The subject and bockground
feothers,
sharp focus, making it difficutt discem fine details. 2. Lighting: The lighting seems uneven, with same areas overexposed
37M
0.23
underexposed, particutorly around the subject's face and the microphone.
SR0C
PLCC
AGIOA-3K
Color Balance: The colors appear somewhat muted or distorted, possibly due to the lighting conditions settings.
Voua Du
04:0
4. Noise: There is visible noise or graininess in the small specks
which be
plumage
especially in dorker areas.
1NC
Overail, the image fucks clarity and detail, which affects quality Improvements could focus, more consistent lighting, and patentiolly adjusting color balance reducing noise.
SROC
PLOC
Figure VisuatQuality-II RI excels at image quality scoring, while generating contextually rich, human-aligned quality descriptions.
Abstract
DeepSeek-RI has demonstrated remarkable effectiveness in incentivizing rea- soning and generalization capabilities of large language models (LLMs) through reinforcement learning. Nevertheless, the potential of reasoning-induced computa- tion has not been thoroughly explored in the context of image quality assessment (IQA), a task depending critically on visual reasoning. In this paper, we introduce VisualQuality-RI a reasoning-induee no-reference IQA (NR-IQA) model, and we train it with reinforcement learning to rank, a learning algorithm tailored to the intrinsically relative nature visual quality. Specifically, for pair of images, we employ group relative policy optimization to generate multiple quality scores for each image. These estimates are used to compute comparative probabilities
"Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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of one image having higher quality than the other under the Thurstone model. Rewards for each quality estimate are defined using continuous fidelity measures rather than discretized binary labels. Extensive experiments show that the proposed VisualQuality-Rl consistently outperforms discriminative deep learning- NR-IQA models as well as recent reasoning-induced quality regression method. Moreover, VisualQuality-RI is capable of generating contextually rich, human- aligned quality descriptions, and supports multi-dataset training without requiring perceptual scale realignment. These features make VisualQuality-R especially well-suited for reliably measuring progress in wide range of image processing tasks like super-resolution and image generation.
1 Introduction
Image quality assessment (IQA) aims to quantify the visual quality of digital images consistent with human perceptual judgments. Commonly, IQA models are classified into full-reference (FR) and no- reference (NR) approaches [47], depending this paper, we focus on NR-IQA due to its practical relevance in real-world scenarios where reference images are unavailable. Over the decades, NR-IQA has evolved from knowledge-driven [33, 12] to data-driven approaches [30, 19, 54], and shifted from regression-basee to ranking-based [58, 59] techniques. Nevertheless, achieving strong model generalization (e.g. generalization to unseen image distortions) remains a significant, unresolved challenge, driving recent research toward multi-dataset training [6]. active fine-tuning [44], and continual model adaptation [57].
the availability of pristine-quale qualltyy reference images. In
The rapid advancement of lision-language models (VLMs) offers promising avenues for enhancing NR-IQA generalization by contextualizing it into broader vision tasks [51]. VLMs effectively integrate multi-modal information, enabling understanding of both low-level image distortions (e.g.. noise and blur) and high-level perceptual attributes (e.g., aesthetics and content semantics). This multi-modal semantic contextualization allows VLMM to articulate nuanced quality descriptions with stronger generalization.
However, current NR-IQA methods mainly leverage VLMs through supervised line-tuning (SFT), which face several critical limitations [49, 56]. First, constructing informative quality descriptions demands extensive human effort, rendering the annotation process labor-intensive and expensive'. Second, models trained via SFT often overfit to the biases and idiosyncrasies present in training data, and may unintentionally encounter catastrophic forgetting of acquired knowledge during pre-training. Third, SFT typically yields overly rigid and templated outputs (see Fig. 1) that may be less useful.
Reinforcement learning (RL) has recently emerged as powerful alternative, enhancing the reasoning capabilities of I.LMs, while aligning their responses with human preferences [35, 13]. In particular, DeepSeek-RR [13] demonstrates the effectiveness of RI. in promoting generalization by encouraging automated exploration of plausible reasoning paths and employing rule-based rewards to prevent reward hacking [37]. However, a direct adaptation of RL techniques to NR-IQA, as exemplified by the recent Q-Iiitght model [21], has been limited by its reliance on dataset-specific reward design and additional distortion-yy tyyp classification. These constraints stem from its treatment of visual quality as an absolute perceptual quantity, thereby framing -R-IQA naïvely regression task.
In this paper, we introduce VisualQuality-R1, a reasoning- induced NR-IQA model, and we train via reinforcement learning to rank (RI.2R), learning algorithm explicitly designed to capture the inherently relative nature of visual quality. Specifically, we employ group relative policy optimization (GRPO) [36] to derive multiple quality scores for each image in pair. We then compute comparative probabilities between images using the Thurstone model [41] by assessing the difference between the mean quality score of one image and individual quality scores of another, normalized by their sample variances. Unlike previous methods, we define the reward function using the continuous fidelity measure [42], which provides precise guidance to facilitate quality ranking. Extensive experiments confirm that VisualQuality- R1 effectively assesses visual quality across a diverse range of distortion scenarios, outperforming discriminative deep learning- based NR-IQA models well as a recent cewsoonin--nnuucee quality regression method [21]. Moreover, VisualQuality-P R1 generates
Utiliizng thte-aof-the-at proprietary VLMs such as GPT-4o [16] for automated annotation suffers from similar scalability challenges due to high computational costs and financial burdens.
2

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contextually rich, ouman-aligned quality descriptions (see Fig. 1), which can be leveraged to provide targeted feedback for downstream image processing algorithms, and support fine-granee fineggrineed quality control in digital photography pipelines. Additionally, we demonstrate that VisualQuality-R remains effective across multi-dataset training scenarios without requiring perceptual scale realignment.
2 Related Work
This section provides a structured review of related NR-IQA models, emphasizing recent advance- ments, particularly those leveraging VLMs.
Regression- based Models NR-IQA models primarily employed regression-basee approaches, wherein image quality was treated as an absolute perceptual quantity directly estimated from extracted "quality- aware" features. Initially, features were handcrafted based natural scene statistics [33, 34), degradation-specine characteristics [46, 48, 24), and perceptual models inspired by the human visual system [45]. Nonetheless, these methods were limited by the representational capacity of handcrafted features. Later, deep learning-based regression models emerged as the dominant paradigm, using end-to-end trainable neural networks directly predict quality scores (or, in some cases, quality distributions) [18, 30, 3, 39, 54, 52]. These models typically utilize standard regression losses such as the mean squared error and mean absolute error, or statistical distances such as the earth mover's distance [39] and Hullback-Leibbee (KL) divergence [55]. Regression- -aaedd models often struggle with generalization issues, and require labor-intensive perceptual scale realignment [31] when training on multiple IQA datasets.
Ranking-based Models To address these shortcomings, ranking-based NR-IQA models were
introduced, modeling visual quality as an intrinsically relative perceptual quantity. Gao et al. [10] pioneered the concept of quality ranking in NR-IQA, although their initial implementation relied on predefined anchor images and was not end-to-end optimized. Ma al. [29] adapted RankNet [4] to NR-IQA by training (though not fully end-to-end) on quality- didiiit-disrimmnablee image pairs. Their subsequent work established the first end-to-end ranking- baseng-based NR-IQA method grounded in the Thurstone model [41]. Nevertheless, their approach suffers from scaling ambiguity during variance estimation. Zhang et al. [58] incorporated hinge loss regularize variance estimation, yet the scaling ambiguity persisted. Their study also demonstrated the superiority of the fidelity loss [42] over the conventional cross-entropy loss in ranking-based NR-IQA Subsequent research has adopted simpler approach by fixing the variance parameter to one (corresponding to the Thurstone Case model), facilitating active fine-tuning of NR-IQA models on challenging examples [44, 43] and allowing for continual adaptation to novel distortion scenarios [57]. Other losses that enable quality ranking include the margin ranking loss [25], differentiable approximations of Spearman's rank correlation coefficient (SRCC) [2]. Pearson linear correlation coefficient (PLCC) [53], and statistical distances between permutation probabilities [5, 38, 17].
-LM-b Models The integration of VLMs into NR-IQA has recently gained traction, particu- larly due to their proficiency in capturing contextual semantics through multi-modal representation learning. Early attempts include multitask adaptation of CLIP [59], as well as SFT-based meth- ods like Q-Align [50]. Compare2Score [60]. DepictQA [56], and DeQA-Score [55], which trained VLMs to generate either quality scores, distributions, or descriptions. Closest to ours, Q-Insight [21] explored reasoning- inducedd quality regression through RL. However, Q- Q-Insight struggles with the dataset-specific reward calibration, added complexity of auxiliary distortion-type classification, and generalization to novel distortion scenarios. In contrast, our VisualQuality-R redefines the use of VLMs in NR-IQA by shifting from absolute regression to relative ranking, leading to enhanced generalization across distortion scenarios with better quality justifications.
Reasoning-Induced NR-IQA
To harness both the powerful reasoning-inducing capabilities of RL and the intrinsically relative nature of visual quality, we propose an NR-IQA -ussualQallyy-R--aaaa -nnd an RI.2R method of training it that seamlessly integrates the Thurstone model within GRPO. Fig. shows the system diagram of Vis
3

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Text 1+
Response 1
Predicted
Prompt C
π
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Scores
k-th Estimate and Variance
O
O
Preference
Response K
<thinkoThe image appears to be blurry sharpness, which affects the clarity and detail that can discerned [..] s/think--answer 1.222/2ns2errr
Image Z;
VisualQuality-R1
Thurstone Model
Fidelitty Reward
Response
Predicted Scores
Prompt
up an animal's eye, likely similar creature, [.]]
image appears
Mean Estimate and Variance
O
<ithink--answeena 300/aaaserr///
Response
<think-The image appears up an animal's face, likely a fax, given pattern and color <"thinkorcarwwee http////eeeeee/////s//eeeoo
Image J
VisualQuality-Ri
Reinforcement Learning to Rank (RL2R)
Figure 2: System diagram of the proposed VisualQuality-RI trained via RL2R. Given image pair (zy, with a shared text prompt c, VisualQuality-R -RI generates responses. Following GRPO [36], each response includes detailed reasoning process and predicted quality score. To assess relative visual quality, we calculate the asymmetric comparative probability that image z; is perceived better than Fy under the Thurstone model [41]. This involves subtracting the from the k-th score æ,, standardized by their sample variances. A fidelity reward is derived from human preference, providing continuous supervisory signals for policy optimization.
predicted score of æ;
3.1 VisualQuality-RI via RL2R
Given a text prompt and an image x, our goal to fine-tune a pre-trained VLM, with policy To(-le, 22), produce a scalar quality score in the range of [1, following step-by-step reason- ing process, encapsulated within specially designated tags for explicit instruction and enhanced interpretability. The complete structured text prompt is provided in Table
More specifically, for a training batch of images {I1, #2,..., LB. where the minibatch size, we (24)..2(z;)).......
apply GRPO to generate K quality predictions for x,, (2,) output naturally encodes predictive uncertainty, which is crucial for making reliable relative quality ranking. Under the Thurstone model [41], the visual quality of an image is assumed follow Gaussian distribution. Thus, we compute the asymmetric comparative probability for each of the B x (B 1) ordered image pairs subtracting the mean quality score of x, from the k-th quality score of v,, standardized by their sample variances:
9K (2.)]T, This
(x,))) u({(z,)) Pk (x xzz) )- = (o 0² (g(z))))
for i +
(I)
where ((.) is the standard Gaussian cumulative distribution function. )((((;;) and 0² (1(xx)) represent the mean and variance of the quality predictions for Zj, respectively. is a small positive constant avoid any potential division by zero. Compared previous ranking- NR-IQA models that fix the variance parameter in Eq. (1) to one, we explicitly leverage sample variances derived from GRPO. This gives us an opportunity dynamically accommodate predictive uncertainty for different images. Mcanwhile, using the sample mean for quality comparison stabilizes the asymmetric probability estimate and the subsequent reward calculation by appropriately penalizing outlier predictions.
The true preference 2();, is derived from human mean opinion scores (MOSs):
if MOS(x) > MOS(y)
p(r v) = 0.5 if MOS(x) MOS())
otherwise
important aspect of our RI,2R algorithm that we define the reward function rx (*:;) for each quality estimate qu (I:)) as the fidelity measure [42]-a continuous analogue of the discretized binary

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Table 1: Structured text prompt used in VisualQuality-RI.
You are doing the image quality assessment task. Here is the question: What your overall rating on the quality of this picture? The rating should float between and 5, rounded to two decimal places, with I representing very poor quality and 5 representing excellent quality. First output the thinking process in <think> </think> tags and then output the final answer with only one score in <answer> </answer> tags.
reward [13, 21], averaged across all B - image pairs:
(Voler,,) + v p(x,, - pc(2,, Tx(2;)) = B - 371
E
(3)
This continuous reward feedback provides precise guidance during RL2R by capturing subtle varia- tions quality ranking, thus improving generalization across diverse distortion scenarios. We collect K fidelity rewards for z; into the vector r(z;) = [rr (z;), r2(2))..... TK (z4)) and compute the relative advantage ag (2;) by standardizing rewards within group:
(z,) ((r(z.))
(xx);)
(4)
(r(s)))
The final policy update of Ia (-le, ) is guided by the regularized objective in GRPO:
To(oxle, x.) Teen (ok vii)
no(on(c,zz))
{(0)
min
as (1:), clip
e, + an(wi)
BK
Tew (ox
B DeL (fr
(5)
Here, Tha (c))))) denotes the stable reference policy obtained after VLM pre-training, and Te (:lc, x;), )
is the policy from the previous RL2R training epoch, from which we sample K reasoning trajectories {or j The second KL divergence term is approximated by
DKL (To(oooll 23)) =:)ll xz)) = T mx) Tee(oo 25)
(o(oolc,.z) log
(6)
To(oulc,x;)
incorporated to ensure that the updated policy To(- 24) does not deviate excessively from Teer(-le, 2;). is the clipping threshold to prevent large and potentially destabilizing updates to the policy. The coefficient 3 serves as a balancing factor between the reward-weighted likelihood term and the KL regularization term.
We conclude this section by highlighting the key strengths of our VisualQuality-Ri R1. First, VisualQuality-Rl inherits all the advantages of ranking- based NR-IQA models, enabling effec- tive multi-dataset training, active fine-tuning, and continual model adaptation without requiring perceptual scale realignment [31], feature notably absent in regression-based NR-IQA approaches. Second, trained via RI.2R, VisualQuality-R mitigates the scalability and overfitting issues inherent in SFT-based models. Third, Visual VisualQuality-R promises to both improve model generalizability and furnish contextually rich textual justifications alongside numerical quality scores, thereby boosting its practical relevance in real-world IQA applications.
4 Experiments
To validate VisualQualit-RRi. we conduct comprehensive experiments across diverse distortion scenarios, ablation studies key design components, and in-depth analysis of model behaviors.
4.1
Experimental Setups
Competing Models and Training Details Competing methods encompass three categories: 1) handcrafted models: NIQE [34] and BRISQUE [33]; 2) discriminative deep-learning-bot based models:

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Table SRCC and PLCC results of NR-IQA models trained on KADID- 10K. Exceptions include Q-Insight' and VisualQuality-RI which use combined training set (KADID-10K and SPAQ). Top two results are highlighted in bold and underline, respectively.
Rereiing-eeeeeddd Distortion
Processing Related Distortion
Method
De- blurring
Super- Res.
De- hazing
Image Gen.
Avg
BID
CLIVE
KonlQ
SPAQ
SRCC
Handcrofted
NIQE [34]
0,515
0,450
0,421
0,676
0.360
0.557
0.343
0.533
0,482
BRISQUH [33]
0.522
0.314
0.385
0.614
0.389
0.482
0.242
0.497
0.431
Discriminative
Meep-Learning-boee
UNIQUE [58]
0.412
0.470
0.649
0.751
0.669
0.649
0.577
0.608
0.598
MUSIQ [19]
0.327
0.284
0.473
0.720
0.656
0.404
0.458
0.494
0.477
MANIQA [54]
0,420
0,487
0,213
0.745
0.726
0.263
0.608
0.422
0.486
VLM-based
LIQE [59]
0.677
0.719
0,684
0,815
0.797
0.743
0.646
0.653
0.717
Q-Align [50]
0.576
0.554
0.573
0.767
0.761
0.684
0.455
0.682
0.632
DeQA-Score [55]
0.702
0.743
0.677
0.852
0.785
0.710
0.643
0.738
0.731
Qwen2.5-V1.-77 [1]
0.711
0.733
0.754
0.848
0.820
0.603
0.458
0.735
0.708
Q-Inight [21]
0.784
0.761
0,806
0.872
0.831
0.724
0.601
0.749
0.766
Q-Iiiihht
0.806
0.804
0,812
0.907
0.846
0.700
0.539
0.657
0.759
VisualQuality-Rt
0.790
0.750
0.830
0.875
0.838
0.756
0.598
0.775
0.777
VisualQuality-R
0.811
0.811
0.855
0.913
0.845
0.752
0.588
0.754
0.791
PLCC
Handcrafted
NIQE [34]
0.527
0.494
0.439
0.683
0.376
0.587
0.482
0,560
0.519
BRISQUE [33]
0.528
0.362
0.400
0.624
0,380
0.556
0.468
0,541
0.482
Discriminative Meep-Learning-- based
UNIQUE [58]
0.385
0.472
0.590
0.708
0.654
0.668
0.578
0.581
0.580
MUSIQ [19]
0.280
0.325
0,435
0.666
0.563
0.441
0.455
0.434
0.450
MANIQA [54]
0.512
0.571
0,257
0.753
0.728
0.243
0.663
0.448
0.522
VLM-based
LIQE [59]
0.680
0.726
0.652
0.814
0.712
0.775
0.661
0.653
0.709
Q-Align [50]
0.651
0.643
0.612
0.779
0.802
0.713
0.525
0.705
0.679
DeQA-Score [55]
0.743
0.795
0.703
0.858
0.838
0.763
0.688
0.790
0.772
Qwen2.5-V1.-77 [1]
0.725
0.760
0,810
0.854
0.852
0.653
0.553
0.810
0.752
Q-Inighhi [21]
0.796
0.795
0.829
0.872
0,857
0.798
0.669
0,810
0.803
Q- I--iiihht
0.818
0.837
0.809
0.912
0.861
0.779
0.626
0.705
0.793
Visual SsuualQulity-RI
0.806
0.794
0,840
0.878
0.872
0.825
0.651
0.843
0.814
VisualQuality-Rl 0.820
0.844
0.870
0.917
0.879
0.824
0.674
0,820
0.831
UNIQUE (58), MUSIQ [19], and MANIQA [54]: 3) VL.M-based models: LIQE [59], Q-Align [50]. DeQA- -Soore [55], Insight [21], as well as the pre-trained Qwen2.5-VL-77B [1] baseline.
We fine-tune Qwen2.5-VL--7 [1] as the backbone for VisualQuality-R1 using GRPO [36]. The AdamW optimizer [27] employed with an initial learning rate of 10-6 and linear decay schedule. For GRPO, we generate six candidate responses per prompt (i.e., K = 6) and set the balance coefficient B to 0.04. Training runs 16 NVIDIA A100 GPUs with a minibatch size of eight per GPU, taking approximately five hours for total of 10 epochs.
4.2 Main Results
Single-Dataset Training We first train NR-IQA models on the synthetic KADID-IOR [23] train- ing set (6 2 2 split while ensuring content independence) and test in a zero-shot setting across eight datasets with distortions arising from digital imaging and (post-) processpro stages: BID [7], CLIVE [11], KonIQ-10k [15], SPAQ [8]. Liu13 (deblurring) [26], SRIQA-Bende (super- resolution) [6]. Min19 (dehazing) [32], and AGIQA-3K (image generation) [20].
The SRCC and PLCCC results presented in Table 2 reveal several key observations. First, all VLM- based models outperform traditional and discriminative deep-learning- ones, with the base Qwen2.5-VL-7B achieving SRCC of 0.708 despite no IQA-specifis training. This underscores

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Table 3: PLCC results of VisualQuality-RI with varying K in GRPO. The default setting is high- lighted in bold.
Imaging-Relate Related Distortion
Processing-Related Distortion
IGenerated Responses
De-
Super-
De-
Image
Avg
BID
CLIVE
KonIQ
SPAQ
blurring
Res.
hazing
Gen.
0.805
0.795
0.839
0.875
0.875
0.815
0.643
0.844
0.811
0.806
0.804
0.840
0.879
0.867
0.826
0.639
0.840
0.813
0.806
0.794
0.840
0.878
0.872
0.825
0.651
0.843
0.814
Table 4: Comparison of different Thurstone model variants [41] in GRPO of VisualQuality-Rl
Imaging-Related Distortion
Processing- Related Distortion
Method
De- blurring
Super-
De- hazing
Image Gen.
Avg
BID
CLIVE
KonIQ
SPAQ
Res.
SRCC
Q-Insight [21]
0.784
0.761
0.806
0.872
0.831
0.724
0.601
0.749
0.766
Binary Reward
0.780
0.756
0.821
0.877
0.834
0.748
0.587
0.771
0.772
Probability Average (Eq. (7)
0.785
0.761
0.836
0.875
0.835
0.747
0.574
0.775
0.774
Fixed Variance of One
0.778
0.750
0.818
0.871
0.830
0.744
0.606
0.760
0.770
VisualQualit-RI
0.790
0,750
0.830
0.875
0.838
0,756
0.598
0.775
0.777
PLCC
Q-Insight [21]
0.796
0.795
0.829
0.872
0.857
0.798
0.669
0.8
0.803
Binary Reward
0.790
0.792
0.833
0.876
0.867
0.825
0.646
0.840
0.809
Probability Average (Eq. (7))
0.796
0.797
0.844
0.875
0.861
0.817
0.621
0.831
0.805
Fixed Variance of One
0.791
0.785
0.817
0.873
0.852
0.802
0.655
0.818
0.799
VisualQuality-R
0.806
0.794
0.840
0.878
0.872
0.825
0.651
0.843
0.814
the power of current VLMs in capturing generalizable quality cues. Second, reasoning-induces models such as Q-Insihtt and VisualQuality surpass SFT-based counterparts like Q-Align and DeQA-Score Third, the proposed VisualQuality-R1 achieves the best results on average, validating that RL2R aligns better with human perception of image quality than regression-bastt approaches.
Multi-Datase Training Our RL2R approach enables multi-dataset training without the need for perceptual scale realignment. To exploit this, we train VisualQuality-R11
combination of KADID-1OK [23] and SPAQ [8] (again 6 2 2 split while ensuring content independence). As shown in Table VisualQuality-R11 yields consistent performance gains. Despite minor dip in the image generation scenario, the average SRCC/PLCC rises from 0.777/0. /0.814 to 0.791/0.5 /0.831. In stark contrast, Q-Insight [21] fails to benefit from multi-dataset training due to its inability to address perceptual scale variations KADID-I08 10K uses ratings from 1 to 5, while SPAQ spans 0 to 100.
4.3 Ablation Studies
Effect of K in GRPO We vary the number of generated responses, K, while keeping all other settings fixed during GRPO. Table shows that reducing from six (default) to four or five has only marginal effect, offering favorable trade-off between computational cost and accuracy.
Binary Reward vs. Continuous Fidelity Reward Table shows that, within the same RL2R frame- work, continuous fidelity reward generalizes better than the binary reward adopted in GRPO [36]. Moreover, both reward variants consistently outperform the regression- based QnIngtht [21]. under- scoring the effectiveness of our RL2R optimization.
Thurstone Model Variants To evaluate the effectiveness of mean quality computation in Eq. (1), we compare it with an alternative that averages probabilities across individual quality comparisons:
Px(T,, =
1
K
PR.M (1)) =
1
Σ ==1
K
gx(x)) - qh (z,) (Vo((((.))) + t (2(((z))) +
(7)
K
K
"To enable mutti-dataset training of Inniihht, we linearly rescale MOSs from different 1QA datasets to [1, 6). and apply dataset-agnostic threshold to compute binary rewards.
7

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during GRPO. We randomly select 20 im- ages from each of CLIVE [11], KonlQ- 10k [15], SRIQA-Bench [6], and AGIQA- 3K [20]. At successive training steps, Figure 3: Prediction variability decreases
0.20
CLIVE
KeniQ-10K
Standard Deviation
0.15
SRIQA-Bench
AGIOA-3K
we generate multiple responses per image, compute the std of the predicted quality scores, and plot the average std across images. The uniformly downward trend confirms that VisualQuality-R becomes steadily more stable in assessing image quality as training progresses.
0.10
0.05
50
100
150
200
250
300
350
400
490
Training Step
As reported in Table 4, averaging quality scores rather than probabilities yields higher performance across distortion scenarios, indicating more reliable comparative probability estimates and reward assignments. Taking a step further, we fix the variances in Eq. (7) to one-reducing the model to Thurstone Case V [41]. The constant-variance simplification degrades performance on nearly all datasets. This provides a strong indication that sample variances are capable of capturing the perceptual difficulty of image pairs, thus improving comparison reliability and stabilizing fidelity reward computation. Together, these findings verify that RL2R effectively embeds the Thurstone model within GRPO.
4.4 Further Analysis
Predicted Score Variability over Iterations We randomly sample 20 images from each of CLIVE [11], KonIQ-10k [15], SeiQA--Bencl (super-resolution) [6], and AGIQA-3K (image gen- eration) [20], respectively. successive training checkpoints, we generate multiple responses per image and compute the standard deviation (std) of the resulting K quality scores. As illustrated Fig. 3, the std falls steadily across all datasets, indicating that predictions of VisualQuality-Ri become progressively more stable and confident.
Visual Reasoning Evolution over Iterations Fig. 4 tracks how the visual reasoning capabilities of VisualQuality-RI mature over the course of training. The test image super-resolved by SwinIR [22], which contains subtle, processing-related artifacts, making that the image is "blurry" and "overexposed," but assigns an extremely low score (i.e., 2.00), indicating limited sensitivity to super-resolution artifacts. The base model Dwen2.5-VL-7B [1] swings the opposite extreme: praises the "clear details" and "vibrant colors," declares the absence blur or noise, and outputs inflated score (iee., 4.80). The model clearly over-trusts superficial sharpness cues and misses hidden processing traces. In contrast, the proposed VisualQuality-R progressively refines its visual reasoning and questions the image's ' realism, yet it still values the apparent clarity. By the 200-th step, the description becomes more balanced. It acknowledges the level of detail and clarity, yielding slightly higher but still cautious rating. At the last step, the explanation is now decidedly nuanced. Visual Qualityyal attributes the remaining softness possible filtering structure, labels the appearance "surreal," and reduces the score to 3.00, reflecting judicious penalty for unnatural post-processing. In summary, RL2R guides VisualQuality-R from naïve, superficial remarks to sophisticated, human- human-aligned reasoning that correctly identifies subtle super-resolution artifacts and calibrates quality scores accordingly.
informative probe. Q-Insight notices
iterations. At the 50-th step. it starts to suspect artificial stylization
the object's inherent
5 Conclusion and Discussion
We have introduced VisualQuality-RII a reasoning-indueee NR-IQA model optimized via RL2R, Our approach is grounded in the intrinsic relativity of visual quality, seamlessly integrating the Thurstone model within GRPO to capture predictive uncertainty. By introducing the continuous fidelity reward, VisualQuality-R R1 delivers more precise policy-gradient signals.

---

Q-Insight
<think>The image appears to be somewhat blurry, which affects the clarity and detail that can be discerned. The colors are vibrant, but the lack of sharpness reduces the overall quality. There's also slight overexposure in some areas, which might be due to lighting conditions or camera settings-</thinke <ontwerr2.00</answerr
Qwen2.5-VL- (0-th Step)
<think>The image appears to be of high quality with clear details and vibrant colors. The texture and patterns on the objects are well-defined, suggesting good resolution and lighting. There are no visible distortions or blurriness that would typically indicate lower quality. The contrast and saturation levels seem appropriate, enhancing the visual appeal without overwhelming the
VisualQuality-Rt (50-th Step)
<think>Thhe image appears somewhat abstract or stylized, which might be ideal a realistic quality
assessment. However, clear the details are well-defined, suggesting good resolution. The colors are vibrant and the contrast high, which can be visually appealing but may not reflect naturalistic quality, The image does not contain any discernible text or identifiable objects that could provide further context. context.</hinnk-aaaseee w/think--ansswerr..... 3.00/a/swe.
VisualQuality-R R1 (200-th Step)
<think--The image appears have high level of detail and clarity, with distinct patterns and colors that well- defined. However, there slight lack of sharpness around the edges, which might be due the texture way image was captured. The lighting seems even, the colors are vibrant, contributing positively to the overall quality.s/thinko-answee /a0saarwrr////
VisualQuality-RI (Last (tep))
<think<Thh image appears be close-up some kind of fruit or vegetable with patterned surface. The colors vibrant, but the image seems to have filter applied that gives somewhat surreal appearance. The details are very sharp, which might and contrast are high, making the image visually striking striking-</thinko-e </thinko--ansserr333 00</answer>
nature of subject the processing the image. However, the saturation
Figure 4: Evolution of the reasoning capabilities of VisualQuality-Re R1 on an image super-resolved by SwinIR [22]. Initially, VisualQuality-RI overlooks artifacts and overestimates quality; at later stages, it progressively detects stylization, blur, and filtering effects, yielding more accurate quality scores and human-aligned textual justifications. Zoom in for improved visibility.
Extensive experiments validate that VisualQuality-RI consistently surpasses strong discriminative deep souming-basee methods and reasoning- induued baseline. Notably, it bridges the performance gap between synthetic and realistic distortions, demonstrating robustness to dataset inductive biases and noise. In addition to quantitative improvements, VisualQuality-Rl generates contextually rich, human-aligned quality descriptions, which not only enhance transparency and interpretability but also facilitate user trust and post-hoc diagnosis in downstream tasks, such as content filtering, local enhancement prioritization, and quality-aware image retrieval.
Limitations and Future Directions Despite the generalization capabilities demonstrated by VisualQuality-R1, several limitations and promising research directions merit further discussion. First, specific case of test-time scaling, VisualQuality-RI 1 is slow, expensive, and memory-hungee it may also compound early errors into confidently wrong predictions. It is thus desirable to incorporate sample-adaptive reasoning, rationale compression or distillation, and self-consistency sampling to make VisualQuality-Rl faster, cheaper, and more robust. Second, VisualQuality-Ri relies on a single, fixed text prompt for all images, regardless of the underlying distortion scenario application context. Incorporating application-aware prompt adaptation, for example via learned prompt-tuning or dynamic template selection, could tailor VisualQuality-RI! reasoning and scoring to specific image processing tasks, therefore improving its flexibility and accuracy. Third, VisualQuality-R currently formulated as an NR-IQA model, focusing solely on distorted inputs without access to -ustine--uualty counterparts. It is interesting to extend VisualQuality-R I to a reference- based setting, which allows a (possibly corrupted) reference image [51] potentilll differing in resolution. color gamut, dynamic range, or bit depth- to serve as flexible anchor for content fidelity. Last, we foresee adapting the proposed RL,2R learning algorithm to other perceptual assessment tasks, including image aesthetics assessment [40], human age estimation [14], and perceptual similarity ranking [9]. Collectively, these promising directions aspire to foster more intelligent, transparent, and adaptable perceptual systems.

---

Acknowledgments
This work was supported in part by the Hong Kong ITC Innovation and Technology Fund (9440379 and 9440390) and the PolyU-OPPO Joint Innovative Research Center.
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Appendix
A Generalization Probing via gMAD Competition
The group maximum differentiation (gMAD) competition [28] is model comparison framework designed to evaluate the generalization capability of computational models, particularly in scenarios where exhaustive ground-truth labeling is impractical. In this framework, models take turns serving as the defender, while the remaining models act as attackers. The attackers aim to identify image pairs that the defender assigns similar quality scores to, but which they themselves rate very differently. These "adversarial" pairs are subsequently assessed by human observers, allowing for an efficient and targeted examination of model strengths and weaknesses.
Applying gMAD to compare KADID- 10K-trained VisualQuality-RI and Q-Insight on KonIQ 10k [15] reveals clear performance distinction. VisualQuality-R consistently uncovers perceptual inconsistencies in Q-Insight's predictions. Conversely, when subjected attacks, VisualQuality-Rl demonstrates strong robustness, aligning closely with human perception of image quality. This dual capability-hige aggressiveness in exposing flaws and strong resistance to adversarial challenges- underscores VisualQuality-RI superior generalization, affirming its reliability for real-worke IQA.
B Model Complexity Comparison
We present quantitative comparison of model complexity across representative NR-IQA methods. As shown in Table 5, VisualQuality-RI incurs higher inference costs than discriminative models such MUSIQ [19] and MANIQA [54], as well small-scale VLMs like LIQE [59]. This increased computational demand, however, reflects a deliberate design choice to support fine-grained quality reasoning and robust quality rating across diverse distortion scenarios.
C Additional Visual Reasoning Examples
We provide additional visual reasoning examples to illustrate the perceptual alignment of VisualQuality-RI under both synthetic and realistic distortion scenarios. It should be noted that VisualQuality-RI occasionally assigns quality scores outside the prescribed range of [1,5] (e.g..
Best VisualQuality-Rt
Best VisualQuality-Ri
Best Q-Insight
Q-Insight
Fixed Q-Insight
Fixed Q-Insight
Fixed
Fixed
VisualQuality-R1
VisualQuality--i
Worst VisualQuality-R1
Worst QisualQuality-R1
Worst Q-Insight
Worst Q-Insight
B40
(a)
(b)
(c)
(d)
Figure 5: gMAD competition results between VisualQuality-RI and Qnligtt [21]. (a) Fixed Q- Insight at the low-quality level. (b) Fixed Q-Inithht the high-quality level. (c) Fixed VisualQuality- RI at the low-quality level. (d) Fixed VisualQuality-R R1 at the high-quality level.
21

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Table 5: Model complexity comparison using a 512
384 x image as input.
Inference Time
Inference Memory
Method
#Paramcters
#TFLOPs
UNIQUE [58]
22.32 M
s.02
1.62
0.029
MUSIQ [19]
27.13 M
0.05
1.69
0,026
MANIQA [54]
135.75 M
0.03
2.13
0,217
LIQE (59]
151.28
0,03
2.15
0.131
Q-Align [50]
0.14s
17.1G
1,88
DeQA-Score [55]
8.20
0.11
17.1G
1.98
Q-Insight [21]
8.29 B
2.72
17.6
8.71
VisualQuality-R
8.29 B
2.34
17.6
7.74
score 0.2 for the second example in Fig. 6), which reflects a common limitation in LLMs.
Nevertheless, these deviations have negligible impact the overall quality prediction performance.
22

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Synthetic Distortion Scenario
<think>
The image appears to be out focus, which significantly reduces its clarity and detail. The edges of the rocks and trees are not shorp, and the water lacks definition. This lock of focus effects the overall perception of the making difficult discern finer details. The lighting seems odequate, but the blurriness overshadows any positive aspects.
</think>
<answer>11.0 000//onswwr>>>
<think>
The imoge appears to be a close-up shot on onimal's eye, likely a deer similar specles, The focus Is shorp the eye, which is the main subject, but the background blurred, indicating shallow depth of field, which is often used emphasize the subject. The lighting seems natural, and there are no visible distortions or artifacts that would degrade the image quality. However, the image does not provide much context or additional detoils about the environment the onimal itself.
</think
<onswer-3.50</answer 50</answer>
<onswer=3.500
<think>
The Image oppears slightly out of focus, which affects shorpness and detoil that be seen. colors vibrant, but the lock clority reduces the overall quality. The composition interesting with the bird perched on the cactus, but the blurriness detrocts from the visual impact. </think>
conswer-2. <onswer-2.5<</answrr
<thini>
image captures beautiful sunset grodient of colors from and yellows near the horizon. The silhouette the landscupe adds depth and contrast. However, the lower part of the image is quite dark, which might obscure some details. The sky's texture and the subtle gradation of colors suggest good exposure for upper port of the image. Overall, the image has pleasing oesthetic but lacks detall the dorker areas.
top to warm oranges
</think>
<answer>3.80 3.00/r/sso////eerr>>>>
<thinks
image appears to somewhat blurry lacks sharpness, offects clarity. lighting seems adequate but not bright, and there's noticeable graininess or nolse that reduces the overall quality. The composition interesting with on architectural subject, but the technical aspects of the photo do not meet high standards.
</think
<onswer>2.88 8000/onswe>>>
<think>
The image appeors to be a digitol illustration or a graphic design rather than photograph. It has clear lines and vibrant colors, which ore typical for digital ort. The details ore sharp, and there's 's good controst between the elements, making visually oppealing, However, the lack depth and realistic textures might suggest that it's not high resolutsttttion photograph but rather stylized grophic. The averall impression that it's a well-crafted image. </think>
<onswer>4.
Donswer24.00///sswerr 00</onswer>
<think
The image is clear and well-lit, with good focus on the rabbit's face and fur details. The background is slightly blurred, which helps to emphasize subject. colors natural, and are visible distortions artifocts. Overail, the image quality oppeors be high. </thinks
<onswer>d. 95</answer>
<think
The image appears to be digital ortwork or heavily edited photograph. It features silhouette of person seeriingly jumping off o cliff into a misty or fogay environment. The lighting is saft and diffused, which gives it the silhouette, making background somewhot blurry, which is common technique used photography to emphasize the subject. The colors muted, and there's lack sharpness, which might be intentional for artistic effect. </thinks
dreamliike quality. The
<onswer>2. 2.0<</nnwwr/>>>>
Figure 6: Visual reasoning samples from VisualQuality-R -1 under the synthetic distortion scenario.
23

---

Realistic Distortion Scenario
<think>
The Image oppears to be taken at night, which can noturally reduce the overall quality due to lower light conditions, However, the building is well-lit, and the details of the architecture are visible. The exposure seems balanced, and there's no significont noise or blurriness that would degrade the imoge further. composition is clear, and the subject (the building) the focal point. </think>
<onswer>3. 3.00///ww//////r>>>>>
<think
The image appeors to overexposed, which means too bright and lacks detail. This makes difficult discern specific features objects within the image. The lock of detail suggests that the image may not have been token under ideal lighting conditions or thot the camera settings were not adjusted properly for the
</think>
<onswer>0. 200/ar00200//nswrr>>
<think>
The image appears to be somewhat blurry, which offects the sharpness and detail that could otherwise be present. The lighting seems adequote but not particularly bright or well-distributed, which might have contributed some of the blurriness. focus entirely clear, as edges dog's features sharply defined. There is also noticeable omount of nolse graininess the imoge, which further reduces its quality.
</think>
conswer> 1.00/r/swe.////errrr
<think
The image is clear and well-it, showing the pizza in good detail. The colors are vibrant, and the texture of the pizza toppings visible. There are no apparent distortions or blurs that would affect the overall quality. The composition centered, helps focus attention subject. </think>
<onswer=4.874 87</answer>
<think
KFC
The image appears to and well-lit, good focus on KFC logo The colors vibrant, and there noticeable noise blurriness. However, the composition could improved by centering the KFC logo more prominently in the frome. Despite this minor issue, the overall quality of the image is quite good. </think>
foreground.
KEC
<onswer>4, conswer>4.20</anssee 20</answer>
<think>
imuge appears to and good focus on subject. background slightly of focus but still recognizoble natural setting with trees and path. The colors notural and there wisible distortions or ortifacts. The composition is stroightfarward, with the subject centered and prominent. </thinks
<onswer24. Canswera4.3<//answer
<think>
The image is clear and well-itt, capturing the action of two individuals kayaking. The details of their clathing and the kayak are visible, indicating good resolution. The background is slightly out of focus, which helps to does not detract significontly from the overall quality. ore no apparent distortions blurs that would lower the quality. s/think>
emphasize subjects,
<answer>4, Lanswer>4.7x<//nsweer 70</onswer>>
<thinks
The image appeors to taken night in low-light conditions, which evident from the dork background and the presence of bright red lights. The red lights seem be port fireworks or some other form of illumination, but the details are not clear due to the low light and the lack of focus. There also significant clanity, composition
image, which further somewhat obstroct, making difficult discern specific objects events. </think> <answer>2.00 conswer>2.00</onswerr 00</onswer>>
amount of
graininess
Figure 7: Visual reasoning samples from VisualQuality-Rl under the realistic distortion scenario.
24


Reviews:
[{"review_id": "57SryvcahS", "reviewer": "Reviewer_Cn1F", "summary": "The authors propose VisualQuality-R1, a no-reference image quality assessment model that incorporates reasoning through reinforcement learning to rank. Specifically, the method employs GRPO to generate multiple quality estimates per image and leverages the Thurstone model to compute pairwise comparative probabilities. A continuous fidelity reward is introduced to replace traditional binary rewards, aligning better with human perceptual scores. Experimental results demonstrate that the proposed approach outperforms existing SOTA methods across multiple datasets.", "questions": "Have the authors conducted further prompt ablation studies, especially evaluating the effect of using different prompts for different distortion types?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n1. The proposed RL2R framework effectively captures the relative nature of human quality perception, as supported by the empirical results in Section 4. \n2. The introduction of a continuous fidelity reward, combining predicted probabilities and MOS labels, allows for more informative and stable policy updates in GRPO. \n3. Extensive experiments, including evaluations on multiple benchmark datasets and ablation studies, show the method achieves superior performance compared to prior approaches.\n\n### Weaknesses\n1. As shown in Table 4, purely relying on relative scoring does not yield significant performance gains. The improvements are mainly observed when enhanced with components like continuous rewards, adaptive variance estimation, and probability averaging, suggesting the base design alone is insufficient.\n2. Without joint training on multiple datasets, the proposed method achieves only a marginal improvement over Q-Insight, approximately a 1 percentage point gain, raising questions about its generalizability and robustness in single-dataset scenarios.\n3. Given the model's reliance on relative comparisons within a batch, the batch size and sample quality can significantly influence training. It is unclear whether the authors explored the impact of batch construction or optimized sampling strategies.\n4. The paper lacks analysis of computational cost, including training/inference time and memory consumption, making it difficult to assess the method's practicality in real-world deployment.\n5. The use of a single structured prompt for all distortion types may limit the model’s adaptability to diverse and context-specific quality cues.", "was_revised": true, "final_justification": "My concerns have been adequately addressed in the rebuttal. I would like to elevate my rating to 5 (accept) based on the method's potential for its exceptional scalability and elimination of re-annotation requirements. I recommend open-sourcing the implementation to facilitate validation on larger datasets. This would further verify its scaling capabilities while benefiting the community.", "rebuttal": "We are truly grateful for this reviewer's valuable insights and careful evaluation on our manuscript. Please find what below our detailed point-to-point responses to all the comments of this reviewer. We hope our responses can address this reviewer's concerns.\n\n---\n\n**`[W1] Regarding the Base Design`**\n\nWe appreciate the reviewer’s thoughtful concern. However, we respectfully cannot agree that the base design alone is insufficient. To clarify the motivation and demonstrate the effectiveness of our design, which is formulated as pure relative scoring through RL2R, we elaborate on two key aspects below.\n\n1. As shown in Table 4, VisualQuality-R1, which uses only relative scoring without additional components, achieves SRCC of 0.770 and PLCC of 0.799, comparable to Q-Insight’s 0.766 and 0.803. This demonstrates that the core design is already competitive with existing strong methods, validating its effectiveness and soundness.\n\n2. Continuous rewards, adaptive variance estimation, and probability averaging are enhancements based on the our relative comparison framework. As shown in Table 4, all variants are built upon this foundation, demonstrating its modularity and extensibility. This flexibility is a result of our framework’s mathematical grounding and statistical expressiveness.\n\n---\n\n**`[W2] Regarding the Generalizability and Robustness in Single-Dataset Scenarios`**\n\nVisualQuality-R1 and Q-Insight share the same vision-language backbone, Qwen2.5-VL-7B, and they are fully fine-tuned from identical pre-trained weights. The key difference lies in the learning approach: Q-Insight treats IQA as an absolute regression task with binary rewards, while our model adopts a relative formulation using RL2R, applying continuous fidelity-based rewards and uncertainty-aware comparisons based on the Thurstone model. A 1% gain **without architectural changes or pretraining** highlights RL2R’s strength, and consistent improvements across individual datasets confirm its robustness and generalizability in single-dataset settings. We argue that the improvement made by our our method is not marginal.\n\nMore importantly, **RL2R is originally designed for multi-dataset training**, where scale misalignment is a major challenge. As shown in Table 4, simply applying Q-Insight’s regression-based objective to multi-dataset training leads to a significant performance drop. In contrast, our RL2R approach not only maintains performance but achieves substantial gains across datasets, further validating its effectiveness in more challenging settings.\n\n---\n\n**`[W3] Regarding Batch Construction`**\n\nThanks for the insightful comment. We fully agree that batch construction is important for relative comparison models. We analyzed the impact of per-GPU batch size and sample quality, with SRCC results shown in the table below.\n\n\\begin{array}{l|cccc|cccc|c|c}\n\\hline\n\\text{Method} & \\text{BID} & \\text{CLIVE} & \\text{KonIQ} & \\text{SPAQ} &\n\\text{Deblurring} & \\text{Super-Resolution} & \\text{Dehazing} &\n\\text{Image Generation} & \\text{Avg} & \\text{Time} \\newline\n\\hline\n\\text{Q-Insight} & 0.784 & 0.761 & 0.806 & 0.872 & 0.831 & 0.724 & 0.601 & 0.749 & 0.766    & 5\\text{h} \\newline\n\\text{BS=4}      & 0.793 & 0.764 & 0.826 & 0.880 & 0.840 & 0.764 & 0.602 & 0.769 & 0.780  & 8\\text{h} \\newline\n\\text{BS=6}      & 0.793 & 0.768 & 0.830 & 0.881 & 0.841 & 0.755 & 0.607 & 0.764 & 0.780 & 6\\text{h} \\newline\n\\text{BS=8 (Ours)}      & 0.790 & 0.750 & 0.830 & 0.875 & 0.838 & 0.756 & 0.598 & 0.775 & 0.777   & 5\\text{h} \\newline\\hline\n\\text{Same Content} &0.744\t&0.708\t&0.796\t&0.857\t&0.830\t&0.765\t&0.536\t&0.778\t&0.752  & 5\\text{h} \\newline\n\\text{Similar Quality} &\\text{fail}  &\\text{fail} &\\text{fail} &\\text{fail} &\\text{fail} &\\text{fail} &\\text{fail} &\\text{fail} &\\text{fail}   &-  \\newline\n\\hline\n\\end{array}\n\n\n**Impact of Batch Size:** We experimented with per-GPU batch sizes of 4, 6, and 8. We observed that reducing the batch size results in only marginal performance gains (with SRCC improvements within 0.003), but it leads to a substantial increase in training time. For example, the training time increases from 5 hours with BS=8 to 8 hours with BS=4. This inefficiency becomes even more critical under multi-dataset training, where the overall data scale is significantly larger. Therefore, we choose BS=8 to ensure both effectiveness and scalability.\n\n**Impact of Sampling Strategies (Sample Quality):** Our default approach samples images randomly from a single dataset to form each batch. To further investigate the effect of sample quality, we examine two alternative strategies: \n\n1. Same Content Sampling: We sample batches in which all images share the same content but differ in distortion levels, a strategy applicable only to FR-IQA datasets like KADID-10K. As shown in the table (row: Same Content), this leads to lower performance (SRCC: 0.752), indicating that restricting content diversity weakens generalization. We attribute this to the context-sensitive nature of IQA, where diverse content and distortions offer richer visual cues. In contrast, uniform content may cause overfitting to limited artifacts, which runs counter to our model’s emphasis on semantic reasoning across heterogeneous image pairs.\n\n2. Similar Quality Sampling: We test a more extreme strategy by sorting images by predicted quality and sampling consecutive ones to reduce intra-batch variance (row: Similar Quality). This leads to training collapse, with the model assigning nearly identical scores to all inputs. The failure highlights the importance of intra-batch diversity for generating effective learning signals in ranking-based frameworks. Diverse content and distortion levels are essential for maintaining visual contrast and enabling robust gradient updates in RL2R. Additionally, exposing vision-language models to a range of quality cases supports stable convergence under continuous reward supervision.\n\n---\n\n**`[W4] Regarding Computational Cost`**\n\nWe sincerely thank this reviewer for the suggestion. As mentioned in Lines 170–171 of the main paper, our training was conducted on 16 NVIDIA A100 GPUs and took approximately 5 hours. To better address the reviewer’s concern, we provide a detailed summary of memory consumption and runtime for both training and inference in the table below. \n\n\\begin{array}{lccc}\n\\hline\n \\text{Computational Cost} & \\text{Training (BS=8, K=6, 10 epochs)} & \\text{Inference (BS=32)} & \\text{Inference (BS=1)} \\newline\n\\hline\n\\text{Memory} & 68\\text{G per GPU} & 23.8\\text{G per GPU} & 17.6\\text{G per GPU} \\newline\n\\text{Time} & 5\\text{h} & 5.62\\text{s (One-Shot)} & 2.34\\text{s (One-Shot)} \\newline\n\\hline\n\\end{array}\n\nThe table summarizes the memory and time costs for both training and inference, all evaluated on images with a resolution of 512&times;384. Training with batch size 8, K equals 6, and 10 epochs takes about 5 hours and uses 68 GB of GPU memory since the multi-response sampling and pairwise ranking are performed with a 7B model. In inference, the one-shot setting refers to generating a single response per prompt. Under this setting, inference with batch size 32 takes 5.62 seconds and uses 23.8 GB of memory, while batch size 1 takes 2.34 seconds and uses 17.6 GB, which fits within the memory capacity of commonly available GPUs such as the RTX 3090 (24 GB). Please also refer to our responses to **`Reviewer NF6n [W1 & Q1]`** for more details. We will include this information in the revised manuscript.\n\n---\n\n**`[W5 & Q1] Regarding Single Structured Prompt`**\n\nWe sincerely thank this reviewer for the insightful suggestion. We have explicitly listed this as a limitation of our method and highlighted application-aware prompt adaptation as a future direction (please refer to lines 252–256 in our main paper). To further investigate the potential benefit of prompt specialization, we conducted an additional ablation where we kept the training process unchanged and varied the prompt only during inference. Please refer to our responses to **`Reviewer NF6n [W2]`** for the full set of scenario-specific prompts. The updated SRCC results are shown in the table below.\n\n\\begin{array}{l|cccc|cccc|c}\n\\hline\n\\text{Method} & \\text{BID} & \\text{CLIVE} & \\text{KonIQ} & \\text{SPAQ} &\n\\text{Deblurring} & \\text{Super-Resolution} & \\text{Dehazing} &\n\\text{Image Generation} & \\text{Avg} \\newline\n\\hline\n\\text{Fixed Prompt}      & 0.790 & 0.750 & 0.830 & 0.875 & 0.838 & 0.756 & 0.598 & 0.775 & 0.777   \\newline\n\\text{Diverse Prompt} & 0.795  & 0.767  & 0.840  & 0.887\t& 0.837  & 0.742  & 0.588 & 0.778  & 0.779 \\newline\n\\hline\n\\end{array}\n\nThe above results reveal several interesting patterns, analyzed as follows:\n\n**Imaging-Related Distortions (BID, CLIVE, KonIQ, SPAQ):** All the four datasets exhibit consistent improvements. This suggests that adding simple textual cues helps the model contextualize low-level distortions such as blur or noise more effectively.\n\n**Processing-Related Distortions (Deblurring, Super-Resolution, Dehazing, Image Generation):** These types of distortions often involve complex trade-offs, such as the trade-offs between fidelity and perceptual quality. In such cases, a short prompt may not provide sufficient guidance, and the model might already capture most of the relevant cues from the image itself, making the additional textual input either unnecessary or potentially confusing.\n\nIn this ablation, we only vary the inference-time prompt without retraining the model. Greater gains may be possible by incorporating prompt specialization into training (e.g., via prompt tuning or reward-aware generation), which we leave for future work."}, {"review_id": "NPiowwAjIG", "reviewer": "Reviewer_NF6n", "summary": "This paper introduces VisualQuality-R1, a reasoning-induce, no-reference image quality assessment (NR-IQA) model that produces both scalar quality scores (1-5) and natural language justifications. The paper pointed out prior approaches using regression-based approaches which have their limitations and framed IQA as a ranking problem and trained the model using a custom Reinforcement Learning to Rank (RL2R) framework. \nThe model, built on a VLM (Qwen2.5-VL-7B) generates multiple reasoning-score pairs per image using Group Relative Policy Optimization (GRPO). These are used to compute probabilistic comparisons between image pairs via the Thurstone model, which accounts for both mean and variance of predicted scores. Training feedback comes from a continuous fidelity reward, which measures how well the model’s pairwise rankings align with human preferences (Mean Opinion Scores). \nExperiments across eight datasets show that it outperforms existing deep learning and VLM-based IQA methods. It is notable for its ability to generalize across dataset and for producing high-quality justifications for its predictions.", "questions": "1. The paper honestly acknowledges that the method is \"slow, expensive, and memory-hungry\" due to the need to generate multiple responses per image at inference time. Any plan to provide a quantitative comparison of the inference time and computational cost of VisualQuality-R1 versus other leading models?\n\n2. Although the model output rationales, there is no human evaluation or rubric to assess their alignment, usefulness or consistency. What is a plan to move forward with this?\n\n3. Although the paper claims that VisualQuality-R1 can train across datasets without perceptual scale realignment (i.e., without mapping all scores to a common scale), it actually relies on rescaling MOS scores to a common 1–5 range before training. The continuous reward also based on MOS differences assumes access to human scores during training, which limits scalability to unlabeled settings. Can you comment on this?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Overall the paper is a comprehensive paper that presents a significant advancement in the field of image quality assessment. The methodology is well motivated and the results are validated. The work is well written and makes a clear contribution.\n\nStrengths:\nNovel Approach: Combining GRPO with Thurstone-based quality comparisons and continuous fidelity reward is original within IQA.\nMultimodal Reasoning: Demonstrates a step beyond score-only predictions, offering contextual textual explanations.\nComprehensive Evaluation: Benchmarks across eight datasets and various distortion types.\nAblation and Analysis: Includes studies of GRPO response count, reward formulation, and score variance over training.\n\nOpportunities For Improvement:\nComputational Cost Analysis: The paper honestly acknowledges that the method is \"slow, expensive, and memory-hungry\" due to the need to generate multiple responses per image at inference time. While this limitation is stated, a quantitative comparison of the inference time and computational cost of VisualQuality-R1 versus other leading models would provide a clearer picture of the practical trade-offs involved.\nPrompt Robustness: The entire methodology relies on a single, fixed text prompt provided in the paper. The authors propose exploring dynamic prompting as future work, but a small-scale study on the current model's sensitivity to minor changes in the prompt wording could have added valuable insight into its robustness\nModel's Reasoning Lacks Empirical Substantiation: Although the model output rationales, there is no human evaluation or rubric to assess their alignment, usefulness or consistency. \nOverclaim Generalization: Although the paper claims that VisualQuality-R1 can train across datasets without perceptual scale realignment (i.e., without mapping all scores to a common scale), it actually relies on rescaling MOS scores to a common 1–5 range before training. The continuous reward also based on MOS differences assumes access to human scores during training, which limits scalability to unlabeled settings.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank this reviewer for the insightful feedback and the thorough evaluation of our manuscript. Please find what below our detailed point-to-point responses to all the comments of this reviewer. We hope our responses can address this reviewer's concerns.\n\n---\n\n**`[W1 & Q1] Regarding Computational Cost Analysis`** \n\nThanks for the constructive comment. The concern regarding the computational overhead of the proposed model is fully acknowledged. To address this issue, we have added a quantitative comparison of inference time, GPU memory usage, and FLOPs across several representative NR-IQA models. These results will be included in the revised manuscript.\n\n\\begin{array}{l|c|c|c|c}\n\\hline\n\\text{Method} & \\text{Parameters} & \\text{Inference Time (BS=1)} & \\text{Per-GPU Inference Memory} & \\text{TFLOPs} \\newline\n\\hline\n\\text{UNIQUE} & 22.32\\text{M} & 0.02\\text{s} & 1.62\\text{G} & 0.029 \\newline\n\\text{MUSIQ} & 27.13\\text{M} & 0.05\\text{s} & 1.69\\text{G} & 0.026 \\newline\n\\text{MANIQA} & 135.75\\text{M} & 0.03\\text{s} & 2.13\\text{G} & 0.217 \\newline\n\\text{LIQE} & 151.28\\text{M} & 0.03\\text{s} & 2.15\\text{G} & 0.131 \\newline\n\\text{Q-Align} & 8.20\\text{B} & 0.14\\text{s} & 17.1\\text{G} & 1.98 \\newline\n\\text{DeQA-Score} & 8.20\\text{B} & 0.11\\text{s} & 17.1\\text{G} & 1.98 \\newline\n\\text{Q-Insight} & 8.29\\text{B} & 2.72\\text{s} & 17.6\\text{G} & 8.71 \\newline\n\\text{VisualQuality-R1} & 8.29\\text{B} & 2.34\\text{s} & 17.6\\text{G} & 7.74 \\newline\n\\hline\n\\end{array}\n\nAs shown in the table above, VisualQuality-R1 incurs higher inference cost than discriminative models such as MUSIQ and MANIQA, as well as small-scale VLMs like LIQE, primarily due to the large model backbone (Qwen2.5-VL-7B). However, this computational overhead reflects a deliberate design choice aimed at enabling fine-grained, human-aligned reasoning, uncertainty-aware ranking under the Thurstone model, and consistent generalization across diverse distortion scenarios (see Table 2 in the main paper). With the growing popularity and adoption of large-scale VLMs, the increased computational overhead is acceptable.\n\n---\n\n**`[W2] Regarding Prompt Robustness`** \n\nWe appreciate the reviewer’s constructive comment regarding the robustness of fixed prompts. In response, we investigate prompt robustness from two complementary perspectives: 1) introducing new scenario-specific prompt content, and 2) performing controlled perturbations of the original prompt wording. The results will be included in the revised manuscript.\n\n**1) Scenario-Specific Prompt:** As a preliminary step toward addressing this issue, we conducted a small-scale investigation by introducing scenario-specific prompt variants at inference time, without modifying the training configuration. Instead of using a single fixed instruction, we customized the prompt to match the distortion scenario of each test image (e.g., deblurring, super-resolution, dehazing). Specifically, we modified the segment in Table 1 from: *''Rate the overall image visual quality. The rating should be a float between 1 and 5…''* to: *''Rate the overall image visual quality. **[scenario-specific prompt]** The rating should be…''*.\n\nThe ***[scenario-specific prompt]*** is dynamically inserted based on the distortion type. Below are examples of the specialized prompts:\n\n- **Realistic scenario:** *The image may contain naturally occurring distortions from digital imaging pipelines, such as noise, blur, compression artifacts, or color inaccuracy. Evaluate the overall perceived quality as a human would perceive it in everyday photography settings.*\n- **Deblurring:** *This image may have undergone a deblurring process. Evaluate the overall visual quality, including sharpness, clarity, and naturalness. Consider whether the deblurring contributes to a visually pleasing result, or if it introduces artifacts, unnatural textures, or over-sharpening effects. Focus on the image as a whole and assess how perceptually high-quality it appears.*\n- **Super-Resolution:** *This image was enhanced by a super-resolution model. Rate its visual quality based on detail, sharpness, coherence, and realism. Watch for artifacts like over-sharpening, unnatural textures, or structural errors.*\n- **Dehazing:** *This is a dehazed image. Assess the overall visual quality after haze removal, focusing on whether the result appears natural, clear, and visually pleasing. Your evaluation should reflect the image’s clarity, realism, and aesthetic coherence as a whole.*\n- **Image Generation:** *This image is generated by an AI model. Evaluate its overall perceptual quality based on both structural coherence and aesthetic appeal. For aesthetics, consider factors such as composition, color harmony, lighting, visual balance, and artistic style.*\n\nRather than altering the full prompt, we introduced a minimal scenario-specific prefix while keeping the core instruction unchanged. Please refer to our responses to **`Reviewer Cn1F [W5 & Q1]`** for detailed results and analysis. Without modifying training, such prompts bring clear gains for imaging-related distortions, but offer limited improvement for complex processing-related ones, possibly because the model has already captured key cues.\n\n**2) Controlled Perturbations:** We apply prompt perturbations at inference time by randomly introducing minor modifications to the instruction in Table 1, including word substitutions, deletions, and the insertion of special characters. The experimental SRCC results are summarized in the table below. Across all eight datasets, we observe negligible performance differences, suggesting that the model is robust to such minor variations in prompt formulation.\n\n\\begin{array}{l|cccc|cccc|c}\n\\hline\n\\text{Method} & \\text{BID} & \\text{CLIVE} & \\text{KonIQ} & \\text{SPAQ} &\n\\text{Deblurring} & \\text{Super-Resolution} & \\text{Dehazing} &\n\\text{Image Generation} & \\text{Avg} \\newline\n\\hline\n\\text{Original}      & 0.790 & 0.750 & 0.830 & 0.875 & 0.838 & 0.756 & 0.598 & 0.775 & 0.777   \\newline\n\\text{Perturbed} & 0.791  & 0.747  & 0.829  & 0.875 & 0.838  & 0.751  & 0.599 & 0.776  & 0.776 \\newline\n\\hline\n\\end{array}\n\n--- \n\n\n**`[W3 & Q2] Regarding Empirical Substantiation of Rationales`** \n\nWe sincerely appreciate the reviewer’s constructive suggestions. We acknowledge that, to date, there is no well-established metric for accurately evaluating text–image alignment at the low-level quality dimension such as blur, noise, or artifacts, unlike high-level semantic similarity. Hence, we conducted a human study to assess the perceptual alignment of the model’s rationales. We curated a set of 40 test images that were not seen during training, including both natural photographs and outputs from various enhancement algorithms such as deblurring, super-resolution, and AI-generated content. For each image, we collected the model-generated rationale and asked human evaluators to assess whether the reasoning accurately reflects perceptually important quality factors, such as sharpness, contrast, and noise. \n\nEach image-text pair was independently evaluated by 15 domain experts in low-level vision. Following a hard-labeling protocol, we considered a pair to be aligned if at least 10 out of 15 raters deemed the rationale accurate. Each participant took approximately 30 minutes to complete the evaluation. \n\nThe results show that 35 out of 40 image-text pairs (87.5%) were judged as aligned. This provides evidence that the reasoning generated by VisualQuality-R1 is largely consistent with human perceptual judgments, despite the absence of explicit supervision at the rationale level. These results will be included in the revised version of the paper.\n\n---\n\n**`[W4 & Q3] Regarding Overclaim Generalization`** \n\nThanks for the thoughtful comments. This point touches the core design philosophy of RL2R.\n\n**Rescaling MOS Scores:** We clarify that our method **does not require rescaling** MOS scores to a 1–5 range during training. Rescaling is applied only in Q-Insight’s multi-dataset setup (see footnote, page 7 of our main paper). RL2R’s output is constrained to the 1–5 range solely to align with Q-Insight but this does not imply that input score rescaling is required during training.\n\nAs shown in Equation 2, our model relies only on relative comparisons between image quality scores. The continuous reward is based on pairwise preferences, independent of score scale. This relative information can be directly derived from MOS scores in any range (e.g., 0–100 or 1–5), enabling VisualQuality-R1 to train on datasets without perceptual scale realignment.\n\nThis design also enables VisualQuality-R1 to utilize datasets that contain only weak or relative supervision, such as BAPPS or DiffIQA, where fine-grained MOS scores are unavailable. Compared to regression-based methods like Q-Insight, which depend heavily on MOS values and require careful normalization, our RL2R formulation is fundamentally more scalable and robust to diverse labeling conditions.\n\n**Generalization to Unlabeled Settings:** Previous work [42] has shown that unlabeled data can be leveraged via gMAD, where image pairs are selected such that a weaker model rates them similarly while a stronger model detects significant perceptual differences. Building on this, we suggest using VisualQuality-R1 as the student and identifying teacher models with superior performance in certain distortion types or content. gMAD can help mine image pairs from unlabeled data where the teacher sees differences that VisualQuality-R1 misses. These pairs can then be used to fine-tune VisualQuality-R1 and enhance its generalization without human labels.\n\n[42] Active fine-tuning from gMAD examples improves blind image quality assessment, TPAMI 2021"}, {"review_id": "VmiUw0b4wi", "reviewer": "Reviewer_JU4b", "summary": "This paper introduces VisualQuality-R1, a reasoning-based framework for NR-IQA built upon LMM. The model is trained via reinforcement learning to rank images by visual quality, leveraging the GRPO algorithm used in DeepSeek-R1. A key innovation is the use of a continuous fidelity measure, grounded in the Thurstone model, rather than discretized binary labels as rewards. Experimental results across multiple benchmarks demonstrate that VisualQuality-R1 outperforms existing methods, delivering state-of-the-art performance and producing contextually rich, human-aligned quality explanations.", "questions": "Please see the weakness points above.", "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\n- The proposed RL2R algorithm is novel and effectively captures the relative nature of image quality assessment. It enables multi-dataset training without requiring perceptual scale alignment.\n- The paper conducts extensive experiments, including comparisons with recent methods such as Q-Insight, and demonstrates the superior performance of VisualQuality-R1 across several benchmarks.\n- A comprehensive ablation study is presented, clearly analyzing the contributions of different components within the framework.\n- Figure 4 is impressive, illustrating the model's progressive improvement over the course of training and providing qualitative insight into its reasoning capabilities.\n\n## Weaknesses\n\n- Given that reasoning is central to the proposed framework, the paper would benefit from more qualitative comparisons with other methods (e.g., Q-Insight), to better showcase the advantages of VisualQuality-R1 in generating interpretable outputs.\n- Including typical failure cases would enhance the paper’s transparency and help readers better understand the limitations of the proposed method.\n- In Table 4, the performance gap between binary and continuous rewards is relatively small (e.g., 0.772 vs. 0.777 and 0.809 vs. 0.814), which may raise questions about the practical significance of adopting continuous rewards.\n- The multi-dataset training setup only includes two datasets. Incorporating a broader range of datasets could strengthen the generalization claims and provide a more thorough evaluation.\n\n## Conclusion\n\nThis paper presents a novel approach to NR-IQA through the integration of reasoning with reinforcement learning. The proposed RL2R algorithm  is innovative and effective. The experimental results, including strong baseline comparisons and detailed ablations, support the method’s contributions. Overall, this work represents a significant step forward in the field of IQA, enhances the understanding of IQA and provides a promising direction for future research.", "was_revised": true, "final_justification": "The response addressed my concerns well, and I would maintain my original score.\n\nThe proposed RL2R algorithm is innovative and effective. It would be better if the model could be extended to include more datasets and IQA tasks with different prompts, like Q-Align.", "rebuttal": "We sincerely appreciate this reviewer's insightful feedback and careful review of our manuscript. Please find what below our detailed point-to-point responses to all the comments of this reviewer. We hope our responses can address this reviewer's concerns.\n\n---\n\n**`[W1] Regarding Qualitative Reasoning Comparison`** \n\nThanks for the constructive suggestion. We agree that qualitative comparisons can provide valuable insight into the interpretability of model outputs. In the revised version of our manuscript, we will include additional visual examples comparing the reasoning outputs of VisualQuality-R1 with those of Q-Insight and other representative methods to better showcase the strengths of our approach.\n\nAdditionally, we have conducted a user study to assess the alignment of VisualQuality-R1’s rationales with human perception of image quality. Please refer to our responses to **`Reviewer NF6n [W3 & Q2]`**  for more details. The results show that 87.5% image-text pairs were judged as aligned. This provides evidence that the reasoning generated by VisualQuality-R1 is largely consistent with human perceptual judgments, despite the absence of explicit supervision at the rationale level. \n\n---\n\n**`[W2] Regarding Failure Case Demonstration`** \n\nWe sincerely thank this reviewer for this insightful suggestion. As also indicated by **`Reviewer 5jDt [Q1 & Q3]`**, our model indeed exhibits some typical failure cases. For example, in the supplementary material, the second image in Figure B produces a score that falls outside the instructed range, and the sixth image in Figure C lacks style-specific reasoning in its explanation. We will include more failure cases in the revised manuscript. These examples will help illustrate scenarios where the model struggles and highlight directions for future improvement.\n\n---\n\n**`[W3] Regarding Continuous Reward`** \n\nThanks for the insightful comment. To further investigate the practical significance of continuous rewards over binary rewards, we conducted an additional ablation study under a multi-dataset training setting, which differs from the single-dataset setup used in the main paper. Specifically, we combined four heterogeneous IQA datasets: KADID, TID2013, CLIVE, and SPAQ. The SRCC results are shown in the table below.\n\n\\begin{array}{l|cccc|cccc|c}\n\\hline\n\\text{Method} & \\text{BID} & \\text{CLIVE} & \\text{KonIQ} & \\text{SPAQ} &\n\\text{Deblurring} & \\text{Super-Resolution} & \\text{Dehazing} &\n\\text{Image Generation} & \\text{Avg} \\newline\n\\hline\n\\text{Binary Reward}      & 0.822 & 0.817 & 0.844 & 0.911 & 0.849 & 0.743 & 0.614 & 0.752 & 0.794   \\newline\n\\text{Continuous Reward} & 0.829  & 0.824  & 0.845  & 0.916 & 0.856  & 0.740  & 0.662 & 0.757  & 0.804 \\newline\n\\hline\n\\end{array}\n\nThe results consistently show that continuous reward yields higher PLCC scores on 7 out of 8 tasks, leading to a noticeable +1.0% absolute gain on average. Notably, the improvements are more pronounced on challenging cross-domain settings like Dehazing (+4.8%) and Deblurring (+0.7%), where binary rewards tend to saturate. This suggests that **continuous fidelity rewards provide more informative and fine-grained supervision signals**, helping the policy model better differentiate subtle perceptual quality differences during reinforcement learning.\n\nThese findings align with our hypothesis that continuous rewards better reflect the nuanced nature of human perception, which is inherently more graded than binary. As such, even if individual score gaps may seem small, the **consistent directional gain** across diverse tasks and datasets affirms the practical benefit and robustness of the continuous reward design.\n\n---\n\n**`[W4] Regarding Multi-Dataset Training`** \n\nThanks for the constructive comment. To address the concern regarding limited dataset diversity in our multi-dataset training setup, we conducted an additional ablation study by combining four heterogeneous IQA datasets (KADID, TID2013, CLIVE, and SPAQ) into a joint training set. This setup introduces greater variation in distortion types, image content, and rating scales, providing a more rigorous evaluation of model generalization. The SRCC results are presented in the table below.\n\n\\begin{array}{l|cccc|cccc|c}\n\\hline\n\\text{Training Dataset} & \\text{BID} & \\text{CLIVE} & \\text{KonIQ} & \\text{SPAQ} &\n\\text{Deblurring} & \\text{Super-Resolution} & \\text{Dehazing} &\n\\text{Image Generation} & \\text{Avg} \\newline\n\\hline\n\\text{KADID}      & 0.790 & 0.750 & 0.830 & 0.875 & 0.838 & 0.756 & 0.598 & 0.775 & 0.777   \\newline\n\\text{KADID-SPAQ} & 0.811  & 0.811  & 0.855  & 0.913 & 0.845  & 0.752  & 0.588 & 0.754  & 0.791 \\newline\n\\text{KADID-TID2013-CLIVE-SPAQ} & 0.829  & 0.824  & 0.845  & 0.916 & 0.856  & 0.740  & 0.662 & 0.757  & 0.804 \\newline\n\\hline\n\\end{array}\n\nCompared to training solely on KADID (Avg = 0.777) or KADID-SPAQ (Avg = 0.791), training on all the four datasets further improves the average SRCC to 0.804, demonstrating that VisualQuality-R1 benefits from the increased diversity and volume of training data. Importantly, the performance gains are not isolated to a single dataset or distortion type. For instance, we observe improvements on BID and Dehazing datasets, which are known to pose generalization challenges. This confirms that our **RL2R framework remains effective under broader and more heterogeneous training conditions**. We will include this experiment in the revised manuscript."}, {"review_id": "614f2EuSkq", "reviewer": "Reviewer_5jDt", "summary": "This paper proposes VisualQuality-R1, a no-reference IQA (NR-IQA) model that leverages reinforcement learning to rank (RL2R) with a novel integration of the Thurstone model and Group Relative Policy Optimization (GRPO). The core innovation lies in treating visual quality as a relative (rather than absolute) perceptual quantity.", "questions": "- The fixed prompt (Table 1) may hinder adaptation to context (e.g., synthetic art vs. natural images). Fig. C shows scores for stylized images lack nuance (e.g., rating digital art \"4.00\" without style sensitivity).  \n- GRPO’s \\(K=6\\) generations/image imply high costs. No latency/memory metrics are provided.  \n- Fig. B (supplement) shows an implausible score of **0.20** (below the 1–5 range)\n- The conclusion suggests extending to FR-IQA but offers no experiments.  \n\nFinally, if the authors can actively address my concerns, I will be happy to improve the rating score.", "limitations": "Adequately discussed (computational cost, prompt rigidity, error propagation in §9). The suggestion of \"sample-adaptive reasoning\" for efficiency is promising but untested.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "As far as I  am concerened, **Strengths** are:  1) Strong empirical results: Comprehensive experiments (zero-shot generalization, multi-dataset training, ablation studies) validate superiority in SRCC/PLCC metrics (e.g., **0.791** avg. SRCC vs. 0.759 for Q-Insight).  2) Interpretability: Human-aligned textual explanations (Figs. B, C in supplement) enhance transparency, a significant advance over \"black-box\" NR-IQA models.  3)Practical impact: Enables multi-dataset training without MOS realignment, addressing a key bottleneck in real-world deployment.  \n\n**Weaknesses are:**\n\n- Computational overhead: GRPO’s multiple generations per image (\\(K=6\\)) incur high inference costs. Efficiency trade-offs vs. gains are underexplored.  \n- Prompt rigidity: Reliance on a fixed text prompt (Table 1) limits adaptability to distortion-specific contexts (e.g., artistic vs. photographic images).  \n- Baseline depth: Comparisons with recent VLM-based methods (e.g., LLaVA-IQA) are missing. Q-Insight is the only RL-based baseline.\n- Societal impact: While claimed as minimal (§9), potential misuse for automated content moderation (e.g., suppressing \"low-quality\" images) is unaddressed.", "was_revised": true, "final_justification": "Thank you for the author's response. I will maintain my original rating.", "rebuttal": "We are deeply grateful for the reviewer’s thoughtful insights and thorough evaluation of our manuscript. Please find what below our detailed point-to-point responses to all the comments of this reviewer. We hope our responses can address this reviewer's concerns.\n\n---\n\n**`[W1 & Q2] Regarding Computational Overhead`** \n\nThanks for the constructive comment. We would like to clarify that during inference, we generate only one response per image (i.e., K=1), and the corresponding latency and memory usage metrics have been reported in our responses to **`Reviewer Cn1F [W4]`** and **`Reviewer NF6n [W1 & Q1]`**. Please refer to those sections for details.\n\nDuring training, we set the number of generations in GRPO to K=6. To better illustrate the **efficiency trade-offs versus performance gains**, we provide a detailed comparison of training cost and model performance under different generation numbers K=4,5,6. As shown in the table below, increasing K leads to a moderate and manageable increase in both GPU memory usage and training time. Meanwhile, the PLCC improves consistently from 0.811 at K=4 to 0.814 at K=6, suggesting that additional generations bring incremental yet stable gains.\n\n\\begin{array}{lccc}\n\\hline\n & \\text{K=4} & \\text{K=5} & \\text{K=6} \\newline\n\\hline\n\\text{Memory} & 58\\text{G per GPU} & 62\\text{G per GPU} & 68\\text{G per GPU} \\newline\n\\text{Time} & 3\\text{h}39\\text{min} & 4\\text{h} 23\\text{min} & 4\\text{h} 45\\text{min} \\newline\n\\text{Averaged PLCC} & 0.811 &0.813 & 0.814 \\newline\n\\hline\n\\end{array}\n\nThis empirical evidence further motivates our proposed direction of developing more efficient reasoning strategies, such as sample-adaptive generation or rationale distillation, as discussed in the section of **Limitations and Future Directions** in the main paper. We will include this clarification in the revised manuscript.\n\n---\n\n**`[W2 & Q1] Regarding Prompt Rigidity`** \n\nThanks for the valuable and constructive feedback. The limitation of using a fixed prompt has been honestly acknowledged in the section of **Limitations and Future Directions** (lines 252–256 in the main paper). To take a step forward, we designed a small set of scenario-specific prompts based on the original template in Table 1. For the detailed prompt settings, kindly refer to our responses to **`Reviewer NF6n [W2]`** . For the corresponding experimental results and analysis, please refer to our responses to **`Reviewer Cn1F [W5 & Q1]`**. In realistic scenarios, using **adaptive prompts at inference time leads to noticeable improvements**, highlighting the potential of our method to better adapt to distortion-specific contexts.\n\nRegarding the observation that *Fig. C shows scores for stylized images lack nuance (e.g., rating digital art \"4.00\" without style sensitivity)*, we thank the reviewer for pointing this out. We will include this as a representative failure case in the revised manuscript to illustrate the model’s limited sensitivity to artistic style when using fixed prompt. This also underscores the importance of designing scenario-specific prompts to better capture nuanced aspects such as artistic style, which we identify as a promising direction for future work.\n\n---\n\n**`[W3] Regarding Baseline Depth`** \n\nThanks for raising this important point. We agree that including comparisons with recent VLM-based methods is valuable. In fact, our main paper already includes several recent VLM-based NR-IQA baselines that are open-sourced and reproducible, such as LIQE, Q-Align, and DeQA-Score. These represent state-of-the-art approaches that leverage supervised fine-tuning on vision-language backbones. \n\nAs for LLaVA-IQA, to the best of our knowledge, it is only briefly mentioned in a recent non-IQA-specific work ([1] VistaDream, ICCV 2025). To address this concern, we implemented a LLaVA-based baseline under the same evaluation protocol. As shown in the table below, LLaVA-IQA performs notably worse in terms of SRCC than VisualQuality-R1, particularly on distortion-focused datasets such as BID, CLIVE, and Deblurring. This underscores the challenge of adapting general-purpose VLMs to NR-IQA without task-specific training or reinforcement-based optimization. We will include this comparison in the revised manuscript.\n\n\\begin{array}{l|cccc|cccc|c}\n\\hline\n\\text{Method} & \\text{BID} & \\text{CLIVE} & \\text{KonIQ} & \\text{SPAQ} &\n\\text{Deblurring} & \\text{Super-Resolution} & \\text{Dehazing} &\n\\text{Image Generation} & \\text{Avg} \\newline\n\\hline\n\\text{LLaVA-IQA}      & 0.446 & 0.401 & 0.437 & 0.672 & 0.620 & 0.633 & 0.488 & 0.612 &0.539    \\newline\n\\text{VisualQuality-R1} & 0.791  & 0.747  & 0.829  & 0.875 & 0.838  & 0.751  & 0.599 & 0.776  & 0.776 \\newline\n\\hline\n\\end{array}\n\n[1] VistaDream: Sampling multiview consistent images for single-view scene reconstruction, ICCV 2025\n\n---\n\n**`[W4] Regarding Societal Impact`** \n\nThanks for raising this important point. While we initially assessed the societal impact as minimal (§9), we acknowledge that VisualQuality-R1 could be misused in automated content moderation to suppress images deemed “low-quality.” Although our model is designed to **support image enhancement and retrieval, not censorship**, we agree that such risks warrant explicit discussion.\n\nWe will address this in the revised manuscript by highlighting the potential misuse and recommending safeguards, such as human-in-the-loop review and transparency about model limitations. We appreciate the reviewer for bringing attention to this important issue.\n\n---\n\n**`[Q3] Regarding Implausible Score`** \n\nThanks for pointing out this issue. The score of 0.20 in Fig. B indeed falls outside the instructed [1, 5] range, and we sincerely acknowledge this as an instruction-following failure, which is a common limitation observed in large-scale language models.\n\nIn our experiments, we noticed that during the later training epochs (e.g., epochs 9 to 10), the model occasionally produced scores slightly below 1 or above 5. When training continued beyond epoch 10, these anomalies persisted, but they did not noticeably affect overall SRCC and PLCC performance, as both of which are relative ranking metrics.\n\nWe believe there are two plausible reasons for this behavior:\n- Since the model is optimized with a learning-to-rank objective, it first learns to correctly rank images near the boundary scores (e.g., around 1 and 5). After this initial convergence, when it encounters samples that appear significantly worse than previous low-quality images (or significantly better than previous high-quality ones), it may exaggerate the relative differences by assigning scores below 1 or above 5 to maximize the fidelity-based reward.\n- More subtly, during policy optimization, the trade-off between reward maximization and KL regularization may not perfectly constrain the output head to obey soft output bounds. In rare cases, the model may exploit this slack to push scores slightly beyond the expected range.\n\nWe will explicitly document this behavior in the revised manuscript and treat it as a known limitation. One potential remedy is to impose a soft or hard clipping mechanism to enforce outputs within the range without disrupting gradient flow.\n\n---\n\n**`[Q4] Regarding The Extension to FR-IQA`**\n\nThanks for pointing this out. We fully agree that extending VisualQuality-R1 to FR-IQA is a promising research direction, as also outlined in our Conclusion and Discussion section. In this rebuttal, we take a preliminary step toward this goal by adapting our model to FR-IQA settings and conducting initial experiments.\n\nWe begin by modifying the prompt to include explicit reference comparison instructions. Specifically, we adopt the following prompt structure:\n\n*''... Here is the question: The first image serves as the reference for the second image. Please compare the second image against the reference and evaluate its overall visual quality. Provide a quality score as a float between 1.00 and 5.00, ...''*\n\nWe fine-tuned VisualQuality-R1 on KADID-10K in the FR setting and evaluated it on three representative FR benchmarks: Deblurring, Super-Resolution, and Dehazing. The SRCC results are as follows. (Note: ''-FR'' uses both reference and distorted images; ''-NR'' uses only the distorted image.)\n\n\\begin{array}{l|ccc}\n\\hline\n\\text{Method} &\\text{Deblurring} & \\text{Super-Resolution} & \\text{Dehazing} \\newline\n\\hline\n\\text{Qwen2.5-VL-7B-NR}      & 0.820 & 0.603 & 0.458 \\newline\n\\text{VisualQuality-R1-NR}      & 0.872 & 0.825 & 0.651 \\newline \\hline\n\\text{Qwen2.5-VL-7B-FR}      & 0.751 & 0.488 & 0.389  \\newline \n\\text{VisualQuality-R1-FR}      & 0.869 & 0.767 & 0.581 \\newline\n\\hline\n\\end{array}\n\nAs shown in the above table, VisualQuality-R1-FR, fine-tuned with RL2R, significantly outperforms the zero-shot Qwen2.5-VL-7B in the full-reference setting. This demonstrates the effectiveness of our method in adapting VLM to the FR setting.\n\nHowever, we also note that VisualQuality-R1-FR still underperforms its NR counterpart. Similarly, in the zero-shot setting, Qwen2.5-VL-7B shows a notable drop in performance under FR. Upon further qualitative inspection, we found that in the FR setting, the model’s reasoning outputs frequently fail to capture subtle differences between the distorted and reference images. In some cases, the model even **incorrectly concludes that the two images are identical, despite the visible degradations**. These results suggest that reasoning over paired inputs in the FR setting is substantially more challenging and requires targeted learning strategies, which we leave for future work.\n\nThese experiments serve as an early feasibility study toward extending VisualQuality-R1 to FR-IQA. We will include the results and analysis in the revised manuscript."}]

Author rebuttal:

Meta-review: This paper introduces VisualQuality-R1, a no-reference image quality assessment (NR-IQA) model to predict quality scores and text justifications, via Reinforcement Learning to Rank (RL2R) framework.

After rebuttal, all reviewers gave positive review scores, and agreeing on advantages like novel topic of enabling reasoning in IQA area,  technically sound methods (RL2R framework, continuous fidelity reward),  etc, comprehensive evaluation, exceptional scalability and elimination of re-annotation requirements, 

So I will recommend Accept (spotlight).
Decision: Accept (spotlight)