Title: Q-Insight: Understanding Image Quality via Visual Reinforcement Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: image quality understanding

multi-modal large language model

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

Q-Insight: Understanding Image Quality via Visual Reinforcement Learning

Abstract

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

1 Introduction

Image quality assessment (IQA) is a fundamental task in computer vision, critical for optimizing algorithms, enhancing user experiences, and verifying content authenticity across diverse domains, such as image processing [41, 22, 11, 53] and AI-generated content (AIGC) [37, 8]. Traditional IQA methods rely heavily on hand-crafted metrics, either through reference-based comparisons [50] or statistical measures of natural image properties [64]. However, these approaches typically focus on local image characteristics and fail to comprehensively capture global visual quality, limiting their reliability in complex real-world scenarios. More recently, deep learning-based IQA models [47, 19] have emerged, utilizing neural networks to learn hierarchical image representations. Nevertheless, these methods struggle to face significant challenges in out-of-distribution (OOD) generalization.

With recent advances in multi-modal large language models (MLLMs) [23, 1, 48], researchers have begun to leverage these models' extensive world knowledge and perceptual abilities to enhance IQA performance and broaden its applicability [53, 60, 59, 58, 52]. Existing MLLM-based IQA methods generally fall into two categories: score-based methods, such as Q-Align [53] and DeQAScore [58], and description-based methods, exemplified by DepictQA [60] and DepictQA-Wild [59]. Score-based methods transform discrete tokens into continuous quality scores, thereby improving

Reasoning: <think> The image appears to have good lighting,

vibrant colors, and sharpness. It captures a dynamic moment

during an equestrian event, which adds interest and context. The

focus

on

the

horse

and

rider

is

clear,

and

there's

no

apparent

distortion or blur. The background is slightly out of focus, which

helps to emphasize the subject. </think>

<answer> rating: 4.38 </answer>

Question: What is overall quality rating of this picture?

Score Regression

Degradation Perception

Question:

Analyze

the

given

image

and

determine

if

it

contains any distortions and present the distortion level.

Reasoning: <think> The image shows no clear signs of noise,

blur, or darkening. The most noticeable issue is the lack of

fine

detail

and

pixel

repetition,

which

indicates

compression-

resizing or saving the image in a reduced format leads to a blocky

appearance. </think>

<answer> distortion class: compression, severity: obvious </answer>

Image Comparison Reasoning (Zero-Shot)

Reference

Question: Given reference low-resolution image, Image A

Image B is better.

Reasoning: <think> Both images are the high resolution

versions of the same low-quality image. However, Image

B has slightly more detailed textures on the rocks and grass,

indicating

a

higher

level

of

sharpness.

In

terms

of

content

fidelity,

both

images

maintain

the

same

elements

and

composition as the original.

Overall, Image B offers a better balance between detail and

clarity, making it a slightly better choice than Image A.</think>

<answer> Image B </answer>

Image A

Image B

PIPAL

KADID

MUSIQ (ICCV 2021)

AGIQA

CLIP-IQA+ (AAAI 2023)

Q-Align (ICML 2024)

SPAQ

DeQA (CVPR 2025)

Q-Insight (Ours)

CSTQ

LIVE-Wild

adaptability but typically sacrificing interpretability and neglecting MLLMs' intrinsic reasoning and descriptive capabilities. Meanwhile, simply regressing a quality score may not be meaningful in certain scenarios , as image quality scores are subjective, inherently biased, and lack uniform standards across different datasets and content types. For example, when evaluating AIGC-generated data, unusual visual effects and vibrant colors often imply better quality. However, for evaluating super-resolution results, these same features are often considered too painterly, losing the image's authenticity and fidelity. Conversely, description-based methods produce detailed textual explanations of image degradations and comparative assessments, ensuring interpretability yet heavily depending on extensive textual depiction for supervised fine-tuning. Moreover, these models cannot output precise scores, making them unsuitable when used as loss functions or for performing accurate ranking of image quality. Consequently, integrating numerical scoring and descriptive reasoning into a unified, interpretable MLLM-based IQA framework remains an essential yet unresolved challenge.

In this paper, we move towards a comprehensive understanding of image quality by addressing tasks such as image description, aesthetic and compositional evaluation, degradation perception, and comparative reasoning across images. Rather than teaching the large model ' How to score images ', we aim to inspire it ' How to reason deeply and formulate insightful perspectives on image quality metrics during scoring '. To this end, we resort to Group Relative Policy Optimization (GRPO) [40], a reinforcement learning framework inspired by DeepSeek-R1 [15]. GRPO has recently shown to be highly effective in large language models (LLMs). It uses heuristic reward signals to efficiently guide LLMs in uncovering their intrinsic reasoning capabilities, removing extensive reliance on annotated reasoning chains or additional value models. Recently, researchers have also successfully adapted GRPO to vision-language tasks, including few-shot object detection, reasoning grounding [25], and medical analysis [34]. In the context of image quality understanding, the introduction of GRPO provides at least three distinct advantages: (1) no reliance on massive textual training data, (2) strong generalization to OOD evaluated images, and (3) high diversity in supporting multiple tasks. These benefits align well with our goal of developing a generalized image quality understanding agent.

Specifically, we design Q-Insight upon the GRPO framework. In our Q-Insight, we jointly optimize score regression and degradation perception tasks, and carefully design three reward functions: a verifiable score reward for the score regression task, and degradation classification and intensity perception rewards for the degradation perception task. Consequently, Q-Insight effectively exhibits robust reasoning performance using only limited Mean Opinion Scores (MOS) and degradation labels. As shown in Fig. 1, our Q-Insight delivers remarkable performance improvements especially on OOD datasets, while demonstrating comprehensive capabilities across multiple quality assessment and reasoning tasks. For example, it can accurately identify cases where a slightly blurred background, usually regarded as undesirable, effectively helps to emphasize the primary subject of the image. Our empirical investigation reveals that: (1) training solely with score labels results in poor perception of image detail degradations (e.g., JPEG compression), while jointly training with the degradation perception task significantly enhances the model's sensitivity to such degradations, and (2) score regression and degradation perception tasks are mutually beneficial. Extensive experiments across score regression and degradation perception tasks demonstrate that Q-Insight consistently outperforms existing model-based IQA metrics as well as SFT-driven large language models. Moreover, it exhibits impressive zero-shot generalization on unseen tasks, such as image comparison reasoning, highlighting the robustness and versatility of our method. In summary, our contributions are:

❑ (1) We propose Q-Insight, the first reasoning-style multi-modal large language model specifically designed for comprehensive image quality understanding. Unlike previous methods that depend heavily on detailed textual descriptions for supervised fine-tuning (SFT), our approach achieves superior understanding capability using only limited mean opinion scores or degradation labels.

❑ (2) We introduce a unified framework that jointly optimizes image quality rating and degradation perception, revealing mutual benefits across tasks. Within this framework, we develop three specialized rewards, including verifiable score reward, degradation classification and intensity perception rewards, enabling the GRPO framework to effectively generalize to low-level vision applications.

❑ (3) Extensive experiments across diverse datasets and IQA tasks demonstrate that Q-Insight consistently outperforms existing model-based IQA metrics as well as SFT-driven large language models. Moreover, it exhibits impressive zero-shot generalization on unseen tasks, such as referencebased image comparison reasoning, highlighting the robustness and versatility of our method.

2 Related Work

Score-based IQA methods include full-reference and no-reference approaches. Full-reference methods [50, 42, 65] assess image quality by comparing distorted images with high-quality references using traditional metrics (e.g., SSIM [50]) or advanced deep-learning-based metrics [3, 4, 10, 9, 13, 36] like LPIPS [66]. Non-reference methods evaluate quality without reference images, shifting from traditional handcrafted statistics [27, 28, 29, 30, 31, 38] to deep-learning-derived quality priors [18, 19, 24, 33, 44, 73, 74, 45, 49]. Recent multi-modal large language model (MLLM)-based methods, such as Q-Align [53] and DeQA-Score [58], leverage MLLMs' knowledge and perceptual abilities to produce scores. However, they sacrifice the intrinsic descriptive capabilities of MLLMs.

Description-based IQA methods utilize the foundational knowledge of MLLMs to deliver detailed qualitative assessments and improved interpretability [51, 52, 60, 59, 54, 5, 70, 71, 69]. For instance, Q-Bench [51] and Q-Instruct [52] enhance the low-level perceptual capabilities of MLLMs through specialized datasets and tailored evaluation strategies. Co-Instruct [54] specifically focuses on comparative quality assessments among multiple images. Approaches such as DepictQA [60] and DepictQA-Wild [59] handle both single-image and paired-image evaluations across full-reference and no-reference scenarios. Q-Ground [5] emphasizes a detailed visual quality analysis through visual grounding. However, these methods are highly dependent on extensive textual annotations for supervised fine-tuning, leading to considerable costs in human labor or GPT token consumption.

Reinforcement learning (RL) has emerged as an effective strategy to enhance the reasoning performance of LLMs through feedback-driven refinement [7, 43, 40, 55, 57, 17, 68]. Methods like RLHF [32] and RLAIF [2] employ human or AI-generated feedback to refine model behavior. In vision-language tasks, RL has successfully been employed to align model predictions closely with human preferences and reduce hallucinations [46, 61, 62, 72]. Recently, DeepSeek-R1-Zero [15] introduced group relative policy optimization (GRPO) [40], leveraging rule-based rewards to strengthen reasoning capabilities without supervised fine-tuning. Furthermore, Visual-RFT [25] applied GRPO to visual grounding, and Med-R1 [34] adopted GRPO for medical reasoning tasks. R1-VL [63] extends GRPO through step-wise optimization for multi-modal reasoning. Distinctly, our Q-Insight is the first to integrate RL-based strategies into the foundational visual quality understanding model. It jointly trains on multiple tasks and demonstrates mutually beneficial effects among them.

3 Methodology

3.1 Preliminaries

Group Relative Policy Optimization (GRPO) is an innovative reinforcement learning paradigm that has been widely used in models such as DeepSeek R1-Zero [15], achieving excellent results. Unlike Proximal Policy Optimization (PPO) [39], which requires an explicit critic model to evaluate the performance of the policy model, GRPO [40] directly computes the advantage by comparing a group of responses sampled from the policy model, greatly reducing the computational burden. Specifically, given a query q , GRPO samples N distinct responses { o ( 1 ) , o ( 2 ) , . . . , o ( N ) } from the old policy π θ old . Then, the method performs the corresponding actions and receives the respective rewards { r ( 1 ) , r ( 2 ) , ..., r ( N ) } according to the task-specific rules. By calculating the mean and standard

Policy Model

Question:

Analyze the

distortion in the image

and the distortion level.

Question:

What is your

overall rating on the

quality of this picture?

Reference

Model

scr (0)  = 3.91

scr (1)  = 2.57

scr (N-1)  = 4.10

The (i)-th Reasoning Process:

This appears to be a digital

artwork or stylized photograph featuring vibrant colors and

crisp details, though the lighting looks artificial with minimal

depth and texture. Its imaginative composition suggests

artistic rendering rather than photographic capture.

scr (N)  = 3.78

The (i)-th Reasoning Process:

The image shows no visible

noise, and compression artifacts. However, a noticeable lack of

sharpness is evident, likely due to either distant capture or

slight camera movement during exposure. This results in

reduced edge contrast and detail clarity.

deg (0)  = blur

lev (0)  = slight

KL-Loss

Score Regression Reward

Degradation Perception Reward

Multi-Task Group Relative

Policy Optimization

…

deg (1)  = blur

lev (1)  = slight

deg (N-1)  = blur

lev (N-1)  = hard

deg (N)  = dark

lev (N)  = slight

…

Output Group of Score Regression Task

Output Group of Degradation Perception Task

Multi-Task

Query

deviation of the rewards, the relative advantages of each response can be obtained as follows:

where ˆ A ( i ) represents the normalized relative quality of the i -th answer. Overall, GRPO guides the policy model to prioritize higher-quality answers that receive higher reward values within the group. After obtaining the advantage ˆ A ( i ) , GRPO calculates the ratio of the probabilities of each response under the new policy π θ new and the old policy π θ old , denoted as ρ ( i ) . To prevent overly large updates to the model and stabilize training, GRPO restricts the ρ ( i ) to the range [ 1 -δ, 1 + δ ] . To further maintain closeness to the reference distribution π ref, a KL divergence penalty weighted by β is adopted. Finally, the optimization objective of GRPO can be formulated as follows:

where ρ ( i ) = π θ new ( o ( i ) ∣ q ) / π θ old ( o ( i ) ∣ q ) , Q denotes the candidate question set, and D KL denotes the KL regularization. π ref is typically a frozen pre-trained MLLM. GRPO effectively integrates consistent policy updates and strong reward signals in a balanced way. To our knowledge, we are the first to apply GRPO to image quality understanding tasks, enabling our model to achieve robust reasoning and generalization performance without heavy reliance on extensive annotated data.

3.2 Overview of Q-Insight

The overall framework of Q-Insight is illustrated in Fig. 2. During training, we jointly optimize two tasks: score regression and degradation perception. Specifically, the multi-modal input for each task comprises an image paired with a task-specific question. Given these inputs, the policy model π θ generates groups of answers, each accompanied by explicit reasoning steps. Subsequently, each answer is evaluated using its corresponding reward function: R scr for score regression, and R deg and R lev for degradation perception. After computing rewards for each group of answers, the policy model is optimized jointly via the multi-task GRPO algorithm. Additionally, a KL-divergence loss is applied to constrain deviations between the policy model π θ and the reference model π ref. During inference, the trained Q-Insight generates coherent reasoning processes and outputs precise answers. Further details regarding multi-task GRPO and data construction are provided in Sec. 3.3 and Sec. 3.4.

3.3 Multi-Task Group Relative Policy Optimization

As depicted in Fig. 2, for each input data pair, the policy model π θ generates a group of N responses, denoted as { o ( i ) } N i = 1 . We then evaluate each of these responses using the proposed reward functions

( R scr , R deg, and R lev) and obtain the overall rewards { r ( i ) } N i = 1 . Proper reward design is crucial, as informative and carefully constructed rewards directly facilitate Q-Insight's ability to effectively learn reasoning and perception patterns, thus ensuring robust performance across multiple tasks. Specifically, Q-Insight employs a general format reward function shared across all tasks, as well as task-specific reward functions tailored to the unique characteristics of each individual task.

Format reward evaluates whether the reasoning steps are properly enclosed within '<think>' and '</think>' tags, and the final answer is correctly enclosed within '<answer>' and '</answer>' tags [15]. Additionally, we require that the content inside '<answer>' tags follow a JSON-like format: beginning with '{', ending with '}', and containing no additional '{' or '}' characters internally. This ensures that Q-Insight can consistently parse results across different tasks. The reward score r ( i ) fmt is set to 1 if the i -th response fulfills all the above conditions; otherwise, its reward is 0 .

Rewards for score regression task. A standard way to quantify image quality is by using the Mean Opinion Score (MOS). Instead of directly fitting the MLLM predictions to MOS, we use MOS as a general guideline to motivate the model towards deeper reasoning and generating insightful perspectives during the process of evaluating image quality. Inspired by the treatment of mathematical reasoning tasks in DeepSeek-R1 [15], we consider the continuous MOS prediction as either correct or incorrect, thereby adopting a binary reward to avoid extremely large or small reward values. Denoting the predicted score of the i -th response as scr ( i ) pred and the ground-truth score as scrgt, we design the verifiable reward for scoring as follows. The reward value r ( i ) scr for the i -th response is determined by:

where ϵ is a predefined threshold. In particular, if ϵ is set to 0 , the reward simplifies to exact-answer matching. Otherwise, the threshold ϵ allows the model's predicted scores to fluctuate within an acceptable range, rather than strictly requiring exact accuracy. As depicted in Fig. 2, the predicted score receives a reward of 1 if it lies within the threshold ϵ of the ground-truth MOS, and 0 otherwise.

Rewards for degradation perception task. We find that training solely with score labels leads to poor perception of detailed image degradations (e.g., JPEG compression). This may be because generic multimodal models are pre-trained primarily to capture high-level semantic information, causing them to ignore subtle low-level distortions. To address this issue, we jointly train the model with a degradation perception task, leveraging easily obtainable degradation labels, thus enhancing the model's sensitivity to these image degradations. In this task, the model is required to predict both the distortion class and the corresponding distortion level. Since distortion class and level are inherently discrete variables, we similarly design binary rewards for this task. Denoting the predicted distortion class and level of the i -th response as deg ( i ) pred and lev ( i ) pred respectively, we define the degradation classification reward as follows. The reward value r ( i ) deg is determined by:

This means the predicted distortion class receives a reward of 1 if correct, and 0 otherwise, as illustrated in Fig. 2. Similarly, the intensity perception reward r ( i ) lev is determined by:

As depicted in Fig. 2, the predicted distortion level earns a reward of 1 only when both the predicted class and the level exactly match the ground truth; otherwise, it receives 0 .

Overall multi-task reward. Finally, the overall reward of i -th response is calculated as:

where 1 scr equals 1 if the score regression task is selected (and 0 otherwise), and similarly, 1 deg equals 1 if the degradation perception task is selected (and 0 otherwise). Note that the reasoning process illustrated in Fig. 2 emerges naturally from the model's internal capability, without relying on external constraints or additional annotated data. After computing rewards for all generated responses { r ( 1 ) , r ( 2 ) , ..., r ( N ) } , the policy model is updated following Eqs. (1) and (2). With this flexible design, Q-Insight can seamlessly switch between tasks and jointly optimize them during

training. During inference, the trained policy model can directly perform image quality understanding without requiring additional fine-tuning. Experimental results presented in Tabs. 3 and 4 and Fig. 4 further demonstrate that jointly addressing the score regression and degradation perception tasks substantially improves performance, highlighting the beneficial interactions between these two tasks.

3.4 Data Construction

We construct multi-modal training data to jointly train Q-Insight on the score regression and degradation perception tasks. The prompts designed for each task are detailed in Tab. A in the appendix. For the score regression task, the input includes a task-specific prompt and the image to be rated, with the Mean Opinion Score (MOS) serving as the guideline to calculate the corresponding reward. In the degradation perception task, the input consists of a prompt and an image characterized by a specific distortion class and severity level. There are five distortion categories: 'noise', 'blur', 'JPEG', 'darken', and 'null', where 'null' indicates no distortion. Each distortion type has five severity levels: 'slight', 'moderate', 'obvious', 'serious', and 'catastrophic'. The distortion class and corresponding severity level constitute the ground-truth labels to calculate the degradation classification and intensity perception rewards. Overall, our carefully-designed GRPO-based framework and multi-task training strategy help Q-Insight achieve robust reasoning and perception capability even from limited annotated labels. More importantly, this flexible approach facilitates effective generalization to various low-level vision applications, as demonstrated by extensive experiments in Sec. 4, clearly highlighting the advantages of our framework in addressing practical vision tasks.

4 Experiments

4.1 Experimental Setup

Datasets and Metrics. For the score regression task, we use diverse IQA datasets across four categories: (a) in-the-wild datasets, including KonIQ [16], SPAQ [11], and LIVE-Wild [12]; (b)

Q-Insight

Question:

What is your

overall rating on the quality

of this picture? ......

User

<think>

The image shows a clear view of

the neon sign for Amoeba Music

,

which is well-lit against a dark background. The text is legible, and

the colors are vibrant, indicating that the photo has been taken

under good lighting conditions. However, there's

no detail visible

beyond the sign itself

, and the background is not fully captured. The

focus seems to be on the sign rather than the surrounding area.

</think>

<answer>

rating:

3.70

</answer>

Question:

What is your

overall rating on the quality

of this picture? ......

User

<think>

The image appears to

be blurry and unfocused

, which is a significant

issue for image quality. It's

a close-up shot of a dog

, but the lack

of sharpness and detail makes it difficult to appreciate the subject

fully.

The background is also out of focus

, which is distracting. The

lighting seems adequate, but the composition isn't ideal.

</think>

<answer>

rating:

2.60

</answer>

Q-Insight

synthetic distortion datasets, including KADID [22] and CSIQ [20]; (c) model-processed distortions, including PIPAL [14]; and (d) AI-generated images from AGIQA [21]. Following [58], we split KonIQ into training and test sets, with approximately 7000 training images. Mean Opinion Scores (MOS) across these datasets are normalized into the range [ 1 , 5 ] . The remaining datasets are exclusively used to evaluate the model's out-of-distribution (OOD) generalization capability. For degradation perception task, we randomly select 7000 images from DQ-495K [59] that contain a single distortion for training, with an additional 1000 images reserved for testing. We adopt the pearson linear correlation coefficient (PLCC) and spearman rank-order correlation coefficient (SRCC) as metrics to evaluate performance on score regression task, following [19, 53, 58]. For degradation perception, we use the accuracy of distortion class and degradation level as evaluation metrics.

Implementation Details. We adopt Qwen-2.5-VL-7B-Instruct [1] as our base model. In the GRPO algorithm, the generation number N is set to 8 , the weight of KL divergence penalty β is set to 1 × 10 -3 , while the weights α 1 and α 2 are set to 0 . 25 and 0 . 75 , respectively. The threshold ϵ is set to 0 . 35 . We employ AdamW [26] as the optimizer, using an initial learning rate of 1 × 10 -6 that linearly decays to 1 × 10 -9 during training. The model is trained for 10 epochs with a total batch size of 128 . Training is completed in approximately one day using 16 NVIDIA A100 GPUs.

4.2 Score Regression

We first evaluate our Q-Insight on the score regression task. We compare Q-Insight with handcrafted methods NIQE [29] and BRISQUE [28]; non-MLLM deep-learning methods including NIMA [47], MUSIQ [19], CLIP-IQA+ [49], and ManIQA [56]; and recent MLLM-based methods such as C2Score [75], Q-Align [53], DeQA-Score [58], and a supervised fine-tuned Qwen [1]. For a fair comparison, all methods (except handcrafted ones) are trained on the KonIQ dataset, and all MLLMbased methods utilize approximately 7B parameters. The comparison results in terms of PLCC and SRCC between Q-Insight and other IQA methods are presented in Tab. 1. Compared with the state-of-the-art method DeQA-Score, our Q-Insight performs slightly worse on the in-domain KonIQ

p

<think>

The image has good lighting and vibrant colors, which contribute

to its overall appeal. However, there is noticeable noise present,

which can detract from the image quality. The horse and rider

appear

slightly blurry

, indicating motion or a lower resolution.

Despite these issues, the

image still maintains a certain level

of clarity and detail

, especially in the background. Therefore,

I

would rate the image quality as moderate

.

</think>

<answer>

rating:

3.50

</answer>

Question: What's your overall rating on the quality of this picture? The rating should be a float between 1 and 5, rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality.

p

<think>

The image appears to be a digital illustration rather

than a photograph, which can affect its quality.

It has

a somewhat pixelated appearance

, suggesting it might

not be high resolution. The colors are vibrant but the

details are not sharp. The composition is interesting,

capturing an action scene well,

but the lack of detail

and clarity brings down the overall quality

.

</think>

<answer>

rating:

2.70

</answer>

dataset. However, on out-of-distribution (OOD) datasets, Q-Insight consistently outperforms all baseline methods across nearly all benchmarks , achieving approximately 0 . 02 improvements in both PLCC and SRCC. This demonstrates the effectiveness and strong generalization capability of our approach. Fig. 3 illustrates two cases showing the reasoning capability in the score regression task. Specifically, our method goes beyond merely outputting numerical scores and provides detailed, structured reasoning. In the first case (top of Fig. 3), Q-Insight correctly identifies and analyzes textual information displayed on a neon sign, thoroughly examining details such as lighting conditions. In the second case (bottom of Fig. 3), Q-Insight demonstrates its strength in interpreting image composition aspects, such as the arrangement of visual elements and the primary focal point of the image. These examples further illustrate how Q-Insight advances beyond score regression task, offering valuable insights into image quality by examining various perceptual factors from multiple perspectives, ultimately contributing to a comprehensive understanding of image quality.

4.3 Distortion Perception

We further evaluate Q-Insight on the distortion perception task, comparing it with AgenticIR [76], which fine-tunes an MLLM to perform a similar distortion prediction function. The comparative results are presented in Tab. 2. Notably, AgenticIR requires sequential queries for each possible distortion type, whereas Q-Insight identifies distortion types using only a single query . Q-Insight consistently outperforms AgenticIR across nearly all distortion categories, resulting in significantly higher average accuracy. However, for the 'null' category (no distortion), our performance is slightly

Method

KonIQ

SPAQ

KADID

PIPAL

LiveW

AGIQA

CSIQ

ϵ

=

0

.

15

0.918/0.900

0.903/0.903

0.749/0.744

0.443/0.441

0.876/0.847

0.835

/0.764

0.857/0.793

ϵ

=

0

.

25

0.929/0.911

0.902/0.902

0.750/0.752

0.472/0.471

0.884/0.855

0.823/0.765

0.889/0.844

ϵ

=

0

.

30

0.932/0.912

0.906/0.903

0.742/0.738

0.471/0.460

0.887/0.856

0.828/

0.773

0.868/0.822

ϵ

=

0

.

40

0.930/0.912

0.904/0.901

0.721/0.723

0.446/0.428

0.880/0.853

0.819/0.769

0.873/0.831

ϵ

=

0

.

50

0.928/0.906

0.906

/0.902

0.750

/0.746

0.475/0.472

0.885/0.854

0.821/0.768

0.894/0.850

ϵ

=

0

.

35

(Ours)

0.933/0.916

0.907/0.905

0.486/0.474

0.893/0.865

0.811/0.764

0.870/0.824

Question:

Assess the visual quality of both the Image A and the Image B,

explain and justify which one is better considering composition and

degradation. Your answer should be 'Image A' or 'Image B'.

User

<think>

Both images depict a close-up view of what appears to be fur, possibly from an animal.

Image A has

noticeable blur and lacks sharpness

, making it difficult to discern fine details. The composition seems to

focus on the texture of the fur, but the lack of clarity detracts from its aesthetic appeal.

Image B, on the other hand, shows a clearer depiction of the fur's texture and detail

. It has less

blur and more defined edges, suggesting that it has been taken under better lighting conditions or with a

higher resolution camera. The composition is similar to Image A, focusing on the same subject matter.

</think>

<answer>

Image B

</answer>

Ground Truth: Image B

Q-Insight

Image A

Image B

The images depict a seal with a snowy background. In Image A, the contrast is slightly enhanced, making

the seal's fur and features more pronounced. This slight increase in contrast marginally improves the

visual appeal by making the image pop more without significantly altering the content. Image B is slightly

brightened, which can wash out details and make the image appear less natural. Comparing the two,

Image A retains more of the original content's detail and depth

, making it the better-quality image

between the two.

Ground Truth: Image B

DepictQA

lower than AgenticIR, possibly because the reasoning mechanism of Q-Insight is more sensitive to the presence of potential distortions. Overall, these results highlight Q-Insight's ability to efficiently and accurately perceive image distortions through a concise and unified reasoning framework.

4.4 Ablation Studies

Effect of multi-task training. To validate the effectiveness of multi-task training, we compare our jointly trained Q-Insight model with two single-task variants, each trained independently on a single task. The comparison results are presented in Tabs. 3 and 4. As shown in Tab. 3, the jointly trained Q-Insight significantly outperforms the score-only variant on nearly all datasets, especially on datasets involving synthetic distortions (KADID [22], CSIQ [20]) and those containing model-generated distortions (PIPAL [14]). This demonstrates that incorporating the degradation perception task can effectively enhance performance in the score regression task. Fig. 4 further presents the benefits of multi-task training, showing that Q-Insight can precisely identify detailed degradations such as pixel-level artifacts, thereby improving overall accuracy in quality assessment. Similarly, Tab. 4 indicates that in the degradation perception task, our jointly trained model consistently surpasses the degradation-only variant across all distortion types. This suggests that the score regression task also positively contributes to degradation perception capabilities. These experimental results verify the mutual benefit and effectiveness of the proposed multi-task training strategy. Moreover, our findings clearly show that the visual quality understanding potential of MLLMs can be significantly improved through carefully designed training tasks and learning objectives.

Ablation on the score threshold ϵ . Introducing the threshold allows the model's predictions to vary within an acceptable margin. Tab. 5 reports the ablation results for different choices of ϵ . Q-Insight consistently achieves robust and stable performance across various threshold values, demonstrating that its effectiveness does not depend on careful tuning of ϵ .

Reg-Acc and Gen-Acc represent the accuracy between regression-based and generationbased restoration methods, respectively. Q-Insight outperforms score- and description-based methods.

4.5 Image Comparison Reasoning

Our Q-Insight effectively generalizes to zero-shot image comparison reasoning tasks in both referencebased and non-reference-based scenarios, as illustrated in Figs. 1 and 5. Specifically, Fig. 1 shows a reference-based comparison scenario, where the reference image is of lower quality, and Images A and B are outputs generated by two different super-resolution methods. Fig. 5 demonstrates Q-Insight's superiority over DepictQA [60], highlighting its enhanced content understanding and precise perception of degradations. These examples illustrate Q-Insight's robust generalization ability, largely enabled by its RL-based framework and multi-task training strategy.

Furthermore, the comparison reasoning performance can be further boosted by training on a small number of labeled comparison pairs using reinforcement learning. Specifically, we randomly sample 5k data pairs from the DiffIQA [6] dataset, where each pair is labeled only with comparison results, without any textual descriptions. Results are shown in Tab. 6. Our Q-Insight consistently surpasses all score-based and description-based methods in terms of overall accuracy and PLCC/SRCC metrics, demonstrating its promising applicability to various image enhancement tasks. Notably, even the zero-shot version of Q-Insight substantially outperforms DepictQA [60], despite the latter relying on large-scale textual datasets. Additionally, A-Fine [6] utilizes more than 200k data pairs collected from four different datasets, combined with a complex three-stage training pipeline, thus requiring over 40 times more data and considerable effort to develop optimal training strategies. In contrast, Q-Insight achieves superior performance through relatively straightforward yet highly effective visual reinforcement learning. Further details and additional results are provided in Sec. B of the Appendix.

5 Conclusion

In this paper, we introduce Q-Insight, a novel GRPO-based model for comprehensive image quality understanding. It jointly optimizes score regression and degradation perception tasks using only a limited amount of labeled data. Unlike traditional methods that rely on extensive textual annotations or purely numerical scoring, our framework combines numerical accuracy with interpretative reasoning, significantly improving the perceptual analysis capabilities of image quality models. Extensive experiments show that Q-Insight consistently outperforms existing state-of-the-art methods across various datasets and tasks, demonstrating impressive zero-shot generalization and superior comparison reasoning capability. Looking ahead, Q-Insight can extend its capabilities to a wide range of tasks, such as image aesthetic assessments, and serve as a powerful discriminative signal to improve image enhancement models. As a unified model for scoring, perception, comparison, and reasoning, QInsight can act as a central hub, coordinating image reconstruction tools and providing valuable insights into the enhancement process. This integrated and automated system has the potential to revolutionize image quality understanding and enhancement, providing a unified solution that can transform how image quality is evaluated, improved, and applied across various fields.

Limitations. While achieving promising performance, Q-Insight focuses primarily on natural images. Extending to AI-generated images and videos remains essential and is reserved for future exploration. Besides, using a fixed threshold and discrete distortion levels is not the most elegant solution and may allow for more principled approaches. These issues warrant further exploration.

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A Designed Prompts

The prompts designed for each task are detailed in Tab. A. Specifically, a general system prompt is shared across tasks, which encourages the model to explicitly output its reasoning process and provide structured responses. This general prompt is supplemented by task-specific prompts tailored for score regression and degradation perception, respectively. For the score regression task, the input includes a task-specific prompt and the image to be rated, with the Mean Opinion Score (MOS) serving as the ground-truth. In the degradation perception task, the input consists of a prompt and an image characterized by a specific distortion class and severity level. We define five distortion categories: 'noise', 'blur', 'JPEG', 'darken', and 'null', where 'null' indicates no distortion. Each distortion type has five severity levels: 'slight', 'moderate', 'obvious', 'serious', and 'catastrophic'. The distortion class and corresponding severity level constitute the ground-truth labels. Additionally, for the comparative reasoning scenario, the inputs include a prompt, two images to be compared, and an optional reference image.

Table A: Prompts for Different Tasks. The system prompt is shared across all tasks, while taskspecific prompts are additionally designed for each individual task.

System Prompt: A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.

Prompt for Score Regression Task: What is your overall rating on the quality of this picture? The rating should be a float between 1 and 5, rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality. Return the final answer in JSON format with the following keys: "rating": The score.

Prompt for Degradation Perception Task: Analyze the given image and determine if it contains any of the following distortions: "noise", "compression", "blur", or "darken". If a distortion is present, classify its severity as "slight", "moderate", "obvious", "serious", or "catastrophic". Return the result in JSON format with the following keys: "distortion_class": The detected distortion (or "null" if none). and "severity": The severity level (or "null" if none).

Prompt for Non-Reference-Based Image Comparison: Given Image A: <image_A> and <Image_B>, assess the visual quality of both the Image A and the Image B, explain and justify which one is better considering composition and degradation. Your answer should be "Image A" or "Image B".

Prompt for Reference-Based Comparison Reasoning: Given a low-quality reference image and two enhanced outputs. Reference Image: <ref_image>, Image A: <image_A> and Image B: <image_B>. Decide which enhanced image is superior or if they are comparable. Evaluate based on: 1) fidelity and consistency with the reference image; 2) overall perceptual quality. Return exactly one of: "Image A", "Image B", or "Similar".

B Experimental Setups and More Results of Comparison Reasoning

B.1 Datasets

For training, we use the DiffIQA [6] dataset, which contains approximately 180k reference-test image pairs generated by applying diffusion-based enhancement methods to reference images of varying quality. Each reference image is paired with multiple test images, and human annotators provide preference labels through triplet-based comparisons, resulting in roughly 180k comparison pairs. Notably, A-Fine [6] aggregates datasets from DiffIQA, TID2013 [35], KADID [22], and PIPAL [14], totaling over 200k comparison pairs for training. In contrast, we randomly sample only 5k pairs from DiffIQA for our training.

For evaluation, we adopt SRIQA-Bench [6], a benchmark specifically designed to evaluate the generalization ability of IQA models in real-world super-resolution (SR) scenarios. This dataset includes 100 low-resolution (LR) reference images, each enhanced by 10 distinct SR methods encompassing both regression-based and generative models. Human raters perform exhaustive pairwise comparisons within each image group, with each comparison annotated by at least 10 annotators to ensure reliability. Since no ground-truth high-resolution reference images are provided, models must evaluate perceptual quality based solely on the degraded LR images. Thus, SRIQABench poses a challenging scenario to rigorously assess the robustness of full-reference IQA models under imperfect reference conditions. we report Reg-Acc, Gen acc, and overall ACC, which denote pairwise ranking accuracy on regression-based SR methods, generation-based SR methods, and all SR outputs, respectively. These metrics measure alignment between model predictions and human judgments under varying super-resolution styles and distortion characteristics. We also report the PLCC and SRCC of Description-based methods.

B.2 Reward Design and Training Details

In the image comparison task, the model is expected to determine which image is superior or if they are comparable. Denote the predicted score of the i -th response as res ( i ) pred , we design a comparison reward function R comp as follows. The reward value r ( i ) comp for the i -th response is determined by :

Finally, the overall reward of i -th response is calculated as:

where r ( i ) fmt is the format reward. For training, we employ AdamW [26] as the optimizer, using an initial learning rate of 1 × 10 -6 that linearly decays to 1 × 10 -9 during training. The model is trained for 8 epochs with a total batch size of 32 . Training is completed in approximately 20 hours using 16 NVIDIA A100 GPUs.

B.3 More Qualitative Results

We provide quantitative results in Tab. 6 in the main paper. Here we present additional qualitative results of reference-based image comparison reasoning in Figs. A, B, C, and D. It can be observed that, despite training on only 5k comparison pairs, Q-Insight surpasses DepictQA [60] by more effectively recognizing subtle image details and distortions, thereby producing more accurate outcomes.

<think>

Both images appear to be blurry and lack detail when compared to the reference image. There's no

discernible difference between Image A and Image B in terms of fidelity and consistency with the

reference image as both are equally out of focus and do not show any significant details.

</think>

<answer> Similar </answer>

Ground Truth: Similar

Q-Insight

The images depict a section of a brick building with windows and a person standing in the foreground.

Image A, there is a slight blur distortion, reducing the sharpness and detail of the brick texture and the

person. In Image B, a slight darkening is present, which affects the visibility of details but retains the

overall sharpness. Comparing the two, Image B maintains better detail and texture definition despite

being darker, making it the image of higher quality compared to the blurred Image A.

Ground Truth: Similar

DepictQA

Image A (MOS: 1.20)

Image B (MOS:1.00)

。

×

<think>

The comparison of the two enhanced images to the original reference image reveals differences in clarity,

detail, and color saturation. Image A appears slightly more blurred and lacks fine-grained detail, which

could indicate a loss of fidelity. Image B, however, seems to have a clearer and sharper appearance,

preserving the textures and colors more faithfully than Image A.

</think>

<answer> Image B </answer>

Ground Truth: Image B

Q-Insight

The images depict a close-up of a ginger root on a wooden surface. In Image A, there are no visible

distortions, and the image appears clear and sharp. Image B, however, shows a slight desaturation,

which makes the colors appear less vibrant and can affect the visual appeal, especially if the ginger's

natural color is an important aspect of the image. Comparing Image A and Image B, Image A has better

quality due to its lack of distortion and more accurate color representation, making it the superior

image in terms of visual fidelity.

Ground Truth: Image B

DepictQA

Image A (MOS: 2.79)

Image B (MOS:4.60)

。

×

<think>

The comparison of the two enhanced images to the reference image reveals differences in clarity, detail,

and color intensity. Image A appears to have a higher level of detail and clearer textures, while Image B

seems to have a slightly more vibrant color palette but less fine-grained detail.

Both images appear to be enhancements that aim to improve the original image's quality. Image A has a

more balanced enhancement, preserving the structure and details of the building without losing the

natural

colors

and

textures.

Image

B,

however,

seems

to

have

a

more

aggressive

enhancement,

potentially causing some loss of fine detail and a slight increase in noise.

</think>

<answer> Image A </answer>

Ground Truth: Image A

Q-Insight

The images depict a brick building with a distinctive tower against a blue sky. In Image A, there is a

slight brightening distortion which makes the image appear a bit washed out, reducing contrast and

potentially obscuring finer details. Image B has a slight increase in saturation, which enhances the

colors, making them more vivid without significantly altering the image's natural appearance. Comparing

the quality of Image A and Image B, Image B retains better overall quality. The slight saturation boost

in Image B enhances the visual appeal without introducing any noticeable artifacts or degrading the

image's integrity. Therefore, Image B is the better-quality image between the two.

Ground Truth: Image A

DepictQA

Image A (MOS: 2.95)

Image B (MOS:1.27)

。

<think>

The comparison of the two enhanced images to the original reference image reveals differences in clarity,

detail, and color intensity. Both images appear to have undergone some level of enhancement, but there are

subtle distinctions that can be observed. Image A seems slightly more vibrant and detailed than Image B,

but it also appears to have a slight grainy texture that could indicate noise reduction or an increase in

image noise. Image B has a cleaner look with smoother edges and less noise, providing a clearer view of

the train's details and colors. Both images seem to enhance the original image well, but Image B appears

to have a better balance between clarity and noise reduction.

</think>

<answer> Image B </answer>

Ground Truth: Image B

Q-Insight

The images depict a red train in a landscape with trees and grass. In Image A, the slight darkening

reduces visibility slightly but retains most details. In Image B, the slight blur affects the sharpness,

making details less distinct, especially noticeable on the train and foliage.

Comparing the two, Image A maintains more detail and overall content integrity despite the darkening,

making it the better-quality image compared to Image B, which suffers from a loss of detail due to the

blur.

Ground Truth: Image B

DepictQA

Image A (MOS: 1.86)

Image B (MOS:2.94)

DYO SMCO CHS

。

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work mainly addresses the two challenges of current MLLM-based image quality assessment methods: less interpretation of estimated scores and the data-hungry issue. This work applied reinforcement learning-based finetuning on MLLM for image quality understanding. Specifically, three tasks are considered: quality score regression, degradation perception (estimating distortion type and level), and comparison reasoning. The Group Relative Policy Optimization is utilized as the reinforcement learning algorithm to solve the problem. The proposed framework demonstrates a great out-of-distribution generalization performance, and the qualitative results show the proposed framework's potential for a deep understanding of image quality.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1) This is a high quality work, solid technical contribution, well-written and easy-to-follow paper writing, well-explained concepts, and the experimental results support the claims proposed in the paper.\n\n2) The proposed method has been proven data efficient for achieving competitive results with few data points.\n\t\n3) The experimental results show a good generalization capability of the proposed framework on quality score regression tasks. The qualitative results are carefully presented with rich details.\n\nMajor Weaknesses:\n\t\n1) In the paper, the comparison reasoning capability is demonstrated by several qualitative cases comparing the reasoning processes of DepictQA and Q-Insight, which is not sufficient. A large number of well-organized subjective experiments would be needed for a thorough comparison between these two methods, which is a common technique for evaluation in the field of quality assessment.\n\n2) The current setting on distortion types is too simple, only five distortion types are selected, and one of them is \"null\". From Table 4, we can see the model almost achieves 100% accuracy on the distortion classification task. However, distortions are really complicated in quality assessment research, there are multiple types of distortions and it could be the case that multiple distortions exist in the same picture. I feel MLLM may have the potential to be generalized to more complicated distortion scenarios, using few-shot prompting or the generalization ability of the language model. Overall, I think there is still some space to explore looking into this direction.\n\n3) In Table B, the results show that a smaller threshold cannot lead to a better score estimation performance, which is counterintuitive. Can you explain why?\n\n4) The stability issue of RL has not been explored, especially considering that, in image quality assessment, the dataset is comparatively smaller.\n\n5) In Table 1, the authors compared several non-MLLM deep-learning methods. I have several concerns here: 1) more recent deep learning-based methods should be compared, currently, the most updated method is CLIP-IQA+ (AAAI 2023). 2) I feel like it is not a fair comparison because the non-MLLM deep learning models are trained from scratch and are much smaller. Therefore, the comparison can be multiple dimensional by incorporating: a) methods extension with pretraining; and b) training/testing cost comparison.\n\nMinor weaknesses (could be improved, but not necessary to respond):\n\t\n1) Need to add a citation for \"few-shot object detection\"\n\t\n2) Need to explain how the hyper-parameters from line 234 to line 237 are selected.\n\n3) How the inputs and prompts are organized, especially in no-reference or full-reference settings, should be describepd in the appendix.\n\n4) How model is trained is missing. Since there are two different tasks using different datasets, it is not that straightforward to figure out the training procedure.\n\nQUESTIONS:\nCorresponding to the five listed major weaknesses, my questions or suggestions are listed as follows:\n\n1) Could you conduct preliminary subjective experiments on comparing the reasoning capability for DepictQA and Q-Insight? If the experiment design and results are satisfactory, I will increase my evaluation score.\n\n2) Could you analyze the possibility of extending the proposed framework to more complicated distortion types? Furthermore, if you could show the potential for this task by extra experimental results, I will increase my evaluation score.\n\n3) Could you explain why smaller thresholds cannot lead to a better score estimation performance?\n\n4) I suggest the authors add one extra experiment to demonstrate the stability of the RL algorithm.\n\n5) I suggest the authors optimize the experiments design for comparing non-MLLM methods and MLLM methods.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an image quality assessment method using multi-modal large language models (MLLMs). By leveraging Group Relative Policy Optimization (GRPO), the method enables reasoning using MLLMs based solely on ground-truth labels, without requiring any ground-truth reasoning texts. In particular, it combines a Score Regression Reward with a Degradation Perception Reward to fully exploit the potential of MLLMs. Experiments demonstrate that, compared to non-reasoning methods, the proposed approach achieves significantly better generalization performance, especially on unseen datasets.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The idea of applying GRPO to image quality assessment is highly interesting. It reveals a new potential of MLLMs and represents a significant contribution for future researchers. The method is able to illustrate the reasoning process of MLLMs using only ground truth labels, which adds to its practical significance.\n\n2. The proposed reward combines a score regression task and a degradation perception task. This idea is reasonable and contributes significantly to performance improvements.\n\n3. The proposed method shows strong generalization performance, particularly on out-of-distribution (OOD) datasets. This is especially valuable in practical scenarios where test data may differ from the training datasets, making the method more applicable in real-world settings.\n\n**Weaknesses**\n\n1. The authors state, \"For a fair comparison, all methods (except handcrafted ones) are trained on the KonIQ dataset.\" However, the proposed method is trained using both the KonIQ dataset and DQ-495K, which raises concerns about the fairness of the comparison. The \"Ours (Score-Only)\" model in Table 3 is trained only on the KonIQ dataset, and comparisons between this model and existing methods might provide a fairer evaluation. However, in this setting, the \"Ours (Score-Only)\" model underperforms compared to DeQA, which further raises concerns.\n\n2. The paper claims that “the introduction of GRPO provides at least three distinct advantages: … (2) strong generalization to OOD evaluated images.” However, it is questionable whether GRPO itself is responsible for the strong generalization. From Table 3, it appears that the generalization may primarily be due to the joint-training, i.e., the increased training data. The fact that more training data leads to better generalization is not a particularly novel finding.\n\n3. The validity of the model's reasoning is not evaluated. Although Figure 3 presents two examples of reasoning, there is no assessment of whether the reasoning is valid across the entire dataset. If the model happens to produce correct scores based on poor reasoning, it could pose practical issues. DepictQA uses GPT-4 scores and the \"Reasonable rate by human evaluators\" to evaluate the reasoning validity, but they are not employed in this paper.\n\nQUESTIONS:\n1. Could the authors please clarify Weakness 1? If my current understanding is incorrect and the existing methods are actually trained on both the KonIQ dataset and DQ-495K, please indicate so. However, if existing methods are trained only on the KonIQ dataset, please clarify how this evaluation can be considered truly fair.\n\n2. Regarding Weakness 2, could the authors elaborate further? For instance, if GRPO is replaced with multi-task supervised fine-tuning in the proposed method, would the strong generalization performance be lost?\n\n3. For Weakness 3, could the authors provide more explanation? Has it been evaluated whether reasonable reasoning is consistently performed across the entire dataset?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents Q-Insight, a no-reference image quality assessment model optimized using the DeepSeek-R1 framework. Q-Insight employs a multi-task learning approach that jointly performs score regression and degradation perception, enabling improved generalization in quality prediction compared to existing NR-IQA methods. Additionally, it is trained using a pairwise comparison strategy, leading to superior performance over state-of-the-art methods on the SRBench benchmark.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Incorporating a reinforcement learning (RL)-based optimization method into the IQA task is a novel contribution. It alleviates the need for extensive instruction tuning data, thereby reducing reliance on costly human annotations.\n2. Q-Insight demonstrates superior generalization performance compared to both NR-IQA and FR-IQA models, highlighting the effectiveness of the proposed DeepSeek-R1 optimization and multi-task learning framework.\n3. The paper is clearly written, with comprehensive methodological details and well-structured experimental evaluations.\n\nDespite the model’s strong generalization capabilities, several concerns remain:\n\nWeaknesses:\n1. The effectiveness of the proposed multi-task learning framework is constrained in scenarios where degradation labels are unavailable. Constructing IQA datasets is already resource-intensive, and adding degradation perception further increases the annotation burden.\n2. As shown in Table 4, the performance of the baseline model (Qwen2.5-VL-7B) is not reported. Additionally, the results for degradation level prediction are relatively weak compared to degradation classification, raising concerns about the reliability of the degradation-level perception module.\n3. The thresholding mechanism used for decision-making is dataset-specific, which may limit the model’s applicability and performance across diverse IQA datasets.\n\nQUESTIONS:\nQ1: Does DQ-495K contain synthetic data that overlaps with any of the test datasets (e.g., CSIQ)?\nThe performance improvement on CSIQ after joint training is significant, raising concerns about potential data leakage or distributional similarity between DQ-495K and the test set.\n\nQ2: The ablation studies on different threshold settings are crucial for understanding the robustness and sensitivity of the proposed method. These results should be presented in the main text rather than being limited to the appendix.\n\nQ3: There are some typographical errors in the reference list.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Q-Insight, a novel approach to No-Reference Image Quality Assessment (NR-IQA) that emphasizes multi-level natural language explanations rather than scalar scores. Unlike traditional IQA methods that predict numeric score, Q-Insight produces tiered rationales for its judgement:\nOverall Quality\nLocal Artifacts\nSpecific Regions or Areas\nThe model uses BLIP2-FlanT5, a vision-language model combining a frozen image encoder with a Flan-T5 text decoder. It is first fine-funed via Supervised Fine-Tuning (SFT) on synthetic instruction-response pairs derived from human-labeled IQA datasets (e.g., KADID-10K, SPAQ). An optional reinforcement learning extension (Q-Insight-RL) is introduced to further improve rationale generation. \nTo evaluate explanation quality, the authors conduct a human study assessing the faithfulness, specificity, and usefulness of the rationales. The work offers more human-aligned justifications in a more accessible and explanatory format.\n\nSTRENGTHS AND WEAKNESSES:\nOverall, this paper offers a solid and creative contribution to Image Quality Assessment by shifting the focus toward interpretability through structured natural language explanations. While the use of reinforcement learning is minimal, the core SFT-based approach is well-motivated and complements existing IQA literature. Best suited for venues focused on vision-language modeling or HCI. Would benefit from improved benchmarking and analysis of RL’s impact.\n\nStrengths:\nNovel Approach: Reframes NR-IQA as a language-drive, explainable task\nIntroduces multi-level rationales, structured across perceptual dimensions (global, local, and spatial).\nEmploys a modular and low-cost architecture (BLIP2-FlanT5), avoiding full vision-language retraining.\nIncludes a human evaluation study with clearly defined rubrics.\nUses synthetic instruction tuning that scales across existing datasets.\nThe RL variant (Q-Insight-RL) shows a path toward improving alignment through preference-based training.\n\nOpportunities For Improvement:\nNo Scalar Score Prediction: The model does not produce a MOS or DMOS score, which makes direct benchmarking against standard NR-IQA models impossible. Metrics like SRCC/PLCC are not reported.\nGeneralization Challenges: When evaluated across datasets, the model struggles due to perceptual scale mismatches (e.g., different MOS ranges). These challenges are acknowledged but not addressed.\nLimited Role of Reinforcement Learning: RL is not central to the approach. The Q-Insight-RL variant only fine-tunes the text decoder (Flan-T5) using a simple REINFORCE algorithm. The reward is based on agreement with synthetic pairwise preferences, rather than any perceptual fidelity metric (e.g., MOS).\nSynthetic Prompt Limitations: Prompts are derived from heuristic templates based on MOS differences. These may not match real user queries or explanations. More prompt ablation or robustness testing can be beneficial.\n\nQUESTIONS:\nNo Scalar Score Prediction: The model does not produce a MOS or DMOS score, which makes direct benchmarking against standard NR-IQA models impossible. Metrics like SRCC/PLCC are not reported. Any comments on this?\nGeneralization Challenges: When evaluated across datasets, the model struggles due to perceptual scale mismatches (e.g., different MOS ranges). These challenges are acknowledged but not addressed. Can you address more on this?\nLimited Role of Reinforcement Learning: RL is not central to the approach. The Q-Insight-RL variant only fine-tunes the text decoder (Flan-T5) using a simple REINFORCE algorithm. The reward is based on agreement with synthetic pairwise preferences, rather than any perceptual fidelity metric (e.g., MOS). Can you address this?\nSynthetic Prompt Limitations: Prompts are derived from heuristic templates based on MOS differences. These may not match real user queries or explanations. More prompt ablation or robustness testing can be beneficial. Any comments on this?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}