Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions
Authors: Aobo Li
Jinjian Wu
Yongxu Liu
Leida Li
Weisheng Dong
Venue: NeurIPS 2025 poster ()
Keywords: Blind Image Quality Assessment; Data Distribution Reshaping; Synthetic Data
Primary area: applications

Abstract: Blind Image Quality Assessment (BIQA) has advanced significantly through deep learning, but the scarcity of large-scale labeled datasets remains a challenge. While synthetic data offers a promising solution, models trained on existing synthetic datasets often show limited generalization ability. In this work, we make a key observation that representations learned from synthetic datasets often exhibit a discrete and clustered pattern that hinders regression performance: features of high-quality images cluster around reference images, while those of low-quality images cluster based on distortion types. Our analysis reveals that this issue stems from the distribution of synthetic data rather than model architecture. Consequently, we introduce a novel framework SynDR-IQA, which reshapes synthetic data distribution to enhance BIQA generalization. Based on theoretical derivations of sample diversity and redundancy's impact on generalization error, SynDR-IQA employs two strategies: distribution-aware diverse content upsampling, which enhances visual diversity while preserving content distribution, and density-aware redundant cluster downsampling, which balances samples by reducing the density of densely clustered areas. Extensive experiments across three cross-dataset settings (synthetic-to-authentic, synthetic-to-algorithmic, and synthetic-to-synthetic) demonstrate the effectiveness of our method. The code is available at https://github.com/Li-aobo/SynDR-IQA.

Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions
Aobo Li
Jinjian Wu"
Yongxu Liu Leida Li Weisheng Dong
Xidian University
abli0stu. xidian edu en, [jinjian. wu, wsdong) @mail. ammallxxidan xidian. en, {yongxu. liu, 1dli>8xidian cn
Abstract
Blind Image Quality Assessment (BIQA) has advanced significantly through deep learning, but the scarcity of large-scale labeled datasets remains challenge. While synthetic data offers promising solution, models trained on existing synthetic datasets often show limited generalization ability. In this work, we make key observation that representations learned from synthetic datasets often exhibit a discrete and clustered pattern that hinders regression performance: features of high-quality images cluster around reference images, while those of low low- -wwaullltyy images cluster based on distortion types. Our analysis reveals that this issue stems from distribution of synthetic data rather than model architecture. Con- sequently, we introduce novel framework SynDR-IQA, which reshapes syn- thetic data distribution enhance BIQA generalization. Based on theoretical derivations of sample diversity and redundancy's impact generalization ror, SynDR-IQA employs two strategics: distribution-awer diverse content up- sampling, which enhances visual diversity while preserving content distribution, and density-aware redundant cluster downsampling, which balances samples by reducing the density of densely clustered areas. Extensive experiments across three cross-dataset settings (synthetic- (synthetiett--uuthenti,, touthhentiic sunthetic-o--algoritie synthetictt--syntheri demonstrate the effectiveness of method. The code is available at https:://gii h//ggtttub.... com/ www.aa//////naaaaa
and
Introduction
Blind Image Quality Assessment (BIQA) aims to automatically and accurately evaluate image quality without relying on reference images 2 It plays crucial role in enhancing user experience in multimedia applications, improving the robustness of downstream image processing algorithms, and guiding the optimization of image enhancement methods. However, the BIQA task is challenging due to its complexity and high association with human perception.
In recent years, mainstream BIQA methods have greatly surpassed traditional methods due the powerful representational capabilities of deep learning models. However, the success of deep learning largely relies on large-scale annotated datasets. The high cost of acquiring subjective quality labels limits the growth of existing datasets. The availability of reference images and the controllability of quality degradation synthetic distortions suggest that low-cost data augmentation through artificially synthesized data appears to be a feasible solution. In practice, training directly on existing synthetic distortion datasets results in suboptimal quality representations with limited generalization capabilities.
We observe key phenomenon: models trained synthetic data tend to produce discrete and clustered feature distribution. Specifically, as shown in Fig. [ high-quality image features form
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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60
30
10
10
(b)
(c)
Figure 1: (a), (b), and present UMAP 3 visualizations of the same representations learned from the synthetic distortion dataset KADID-IOK 4 by the baseline model [5]. The three visualizations differ only in color mapping: (a) colors indicate the Mean Opinion Score (MOS) values (higher indicates better quality): (b) colors correspond to reference images; and (c) colors denote distortion types. Representative samples are added in (b) and (c) illustrate the redundancy within high-quality and low-quality clusters, respectively. More visualizations across different backbones and datasets are provided in Appendix A to demonstrate the generality of this phenomenon.
distinct clusters based on reference images, while low-quality image features gather based on distortion types. Medium-quality image features lack smooth transitions and tend to attach to high/low-quality clusters. This discontinuous representation is detrimental to the performance of regression tasks like BIQA 66 7). We believe that this phenomenon is primarily caused by core problems in synthetic distortion datasets:
1) Insufficient content diversity, which is caused by the limited reference images in synthetic distortion datasets. This leads to the model's tendency to overfit, hindering the formation of globally consistent quality representation.
2) Excessive redundant samples, which stem from the distorted images in synthetic distortion datasets being uniform combinations of reference images, distortion types, and distortion intensities. This induces the model to overly focus these repetitive patterns while neglecting broader information, thereby exacerbating the overfitting problem.
To understand these issues thoroughly, we theoretically derive the impact of sample diversity and redundancy on generalization error. Based on this theoretical foundation, we design a framework called SynDR-IQA from novel perspective, which reshapes the synthetic data distribution to improve the generalization ability of BIQA. Specifically, to address the issue of insufficient content Diverse Content Upsampling (DDCUp) strategy. By
diversity, propose Distribution-awan sampling reference images from an unlabeled candidate reference set based on the content distribution of existing training helping the model learn consistent representations across different content. To label the newly generated distorted images, we employ key assumption: similar content under the same distortion conditions should result in similar quality degradation. Based on this assumption, we generate pseudo-labels for the newly generated images refereneing given labeled data corresponding similar reference images in the training set, ensuring the reasonableness and consistency of the generated pseudo-labell labels. To address the issue of excessive redundant samples, we design a Density-ave Redundant Cluster Downsampling (DRCDown) strategy. It identifies high-density redundant clusters the training dataset and selectively removes samples from these clusters while retaining samples from low-density regions. This mitigates the negative impact of redundant samples while alleviating data distribution imbalance, thus helping the model learn more generalizable representations. Our contributions can be summarized as follows:
generate distorted images, increase the diversity of visual instances,

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We observed the key phenomenon that models trained on synthetic data exhibit discrete and clustered feature distributions, and provide an in-depth analysis of the underlying causes. Through theoretical derivation, we demonstrate the impact of sample diversity and redundancy on the model's generalization error.
From novel perspective of reshaping synthetic data distribution, proposed the SynDR- IQA framework, which includes DDCUp strategy and a DRCDown strategy, to enhance the generalization capability of BIQA models.
Extensive experiments across various cross-dataset settings, including ppathetic-to-authentie tontheticto-syythhetii, and toathetic-to-lgoorthmiii validated the effectiveness of the SynDR-IQA framework. Additionally, as a data-based approach, SynDR-IQA can be integrated with existing model-based methods without adding inference costs.
2
Related Work
Deep Learning-bass based BIQA Methods. Deep learning has revolutionized BIQA, leading to signifi-
cant advancements in accuracy and robustness 8 9 Recent works have explored various innovative approaches to address the challenges in this field. Zhu et al. [10] proposed MetalQA, employing meta-learning to enhance generalization across diverse distortion types. Su et al. [T]] introduced a self-adaptive hyper network architecture for adaptive quality estimation in real-world scenarios. Ke et al. [12] developed MUSIQ, a multi-scale image quality transformer processing native resolution images with varying sizes. Saha et al. [33 introduced Re-IQA, an unsupervised mixture-of-exppee approach that jointly learns complementary content and quality features for perceptual quality predic- tion. Shin al. [14] proposed QCN, utilizing comparison transformers and score pivots for improving cross-dataset generalization. Xu et al. [15] demonstrated the effectiveness of injecting local distortion features into large pretrained vision transformers for IQA tasks. Despite these advancements, the success of deep learning- based BIQA methods heavily relies on large-scale annotated datasets. The high cost and teme-consmming nature of acquiring subjective quality labels for real-world images significantly limit the growth of existing datasets. This limitation has prompted researchers to explore the potential of leveraging synthetic distortions to generalize real-world distortions.
Synthetic-to- Reall Generalization in BIQA. Due to the significant domain differences between synthetic distortions and real-world distortions, models trained synthetic distortion data often perform poorly when facing real-world images. To bridge this gap, several studies have explored Unsupervised Domain Adaptation (UDA) techniques. Chen al. (16) proposed curriculum- style UDA approach for video quality assessment, adapting models from source to target domains progressively. Lu et [17] introduced StyleAM, aligning souree and target domains the feature style space, which is more closely associated with image quality. Li et al. [18] developed FreqAlign, which excavates perception-orien -oriente transferability from a frequency perspective, selecting optimal frequency components for aligmment. Most recently, Li et al. [19] proposed DGQA, distortion- guided UDA framework that leverages adaptive multi-domain selection to match data distributions between source and target domains, reducing negative transfer from outlier source domains. However, previous work has neglected the distributional issues of synthetic distortion datasets. In this work, we introduce a novel framework SynDR-IQA to enhance the syn-to-real generalization ability of BIQA by reshaping the distribution synthetic data.
3 Methodology
In this section, we introduce the SynDR-IQA framework, which aims to enhance the generalization capability of BIQA models by reshaping the synthetic data distribution. We begin with a problem formalization for BIQA, establishing the foundational context for our work. Then, we conduct a theoretical analysis exploring the impact of sample diversity and redundancy on the generalization error. Building upon this theoretical foundation, we detail the two core components of SynDR-IQA: Distribution-and Diverse Content Upsampling (DDCUp), which addresses the challenge of limited diversity in synthetic datasets and Density-aware Redundant Cluster Downsampling (DRCDown), which mitigates the issue of data redundancy.
3

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Training
Training Ref. Set
Distribution-wwaro Diverse Content Sclection
Upsampled Ref. Set
Images
Subected
Candidate Ref. Set
clone
Insqee
Generate Pseudo-label
close
Delected Candidate
Iosges
of
Density-aware Redundant
MOS
Training Dataset
Cluster Downsampling
Downsampled Training Dataset
High
Low
Figure This figure shows the core concepts of two strategies in SynDR-IQA. The DDCUp strategy (upper) selects images from candidate reference sets that are similar in distribution but diverse in content to the training reference sets for synthesizing distorted samples. The pseudo-labels of these samples depend on the nearest neighbors of their reference images. The DRCDown strategy (lower) identifies high-density clusters the training dataset and selectively removes samples from these clusters while retaining samples from low-density regions.
3.1 Problem Formalization
BIQA aims to predict the perceptual quality of images without reference. Let X' denote the space of all possible images, and y [0. 1] represent the range of quality scores (for simplicity). The BIQA task formalized as learning function X + y that maps an input image to its quality score. The opti- mal function f* is defined by minimizing the expected risk: f = arg min Exzz,)>> [C( y)), where F is the hypothesis space, D is the true distribution of images and quality scores, and L is loss function. Since the true data distribution inaccessible, in practice, we instead minimize the empirical error on the training dataset D {(x, y))) arg minfer SE L(f y:).
3.2 Theoretical Analysis
The construction of synthetic distortion datasets follows systematic process: applying predefined distortion types at various intensity levels to a set of reference images 4 This generation mechanism exhibits two key characteristics: First, low-intensity distortions produce images nearly identical to their references, while high-intensity distortions generate images predominantly characterized by distortion-see sppcciic patterns (representative visual examples can be found in Appendix (B). Second, the dataset generation typically employs uniform sampling across reference images, distortion types, and intensity levels. These two characteristics jointly lead fundamental issue: samples are drawn from different local distributions rather than following identical sampling from the true distribution, resulting in discretely clustered structure in the distribution space.
To understand how this clustered distribution affects model generalization, we need to extend the classical generalization enror analysis account for samples being drawn from different local distributions rather than the true underlying distribution. To formalize this extension, we model the synthetic dataset = {Ix,, y:) as comprising clusters, where each cluster consists of one i.i.d. sample from the true distribution D. along with its associated k; samples drawn from the corresponding local distribution D, C D. This formal characterization leads to the following generalization bound:

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Theorem 3.1 (Generalization Bound for Clustered Synthetic Data). Let F be hypothesis class of functions f X - y, and D = {[1,,, Vi ) be dataset consisting of m ii.d samples from true distribution D. along with their respective Kę redundant samples drawn from the corresponding local distributions Dj C D. With probability at least 1 6, for all E F, we have:
[R()) - Rempppp) (f) (Fdm(()) 2log(22)) (ogg(22))
2 2log(2/0)
8m
3m
where R(f) is the true risk, Remp (f) the empirical risk, (Fd is the Rademacher complex- ity, and 1) = ni Ei=1 Re is defined as redundancy heterogeneity which quantifies the degree of imbalanced distribution of redundant samples.
According to Theorem 3.1 we can observe that the upper bound of the generalization error is influenced by the Rademacher complexity Rad (F), number of i..dd samples m (also referred to as diverse samples), and redundancy heterogeneity 7). Excluding model-related factors (captured by (F)) the bound reveals that the number of distinct samples m plays primary role in determining the upper bound. Enhancing the sampling of diverse samples can effectively lower this upper bound. Balancing samples to reduce redundancy heterogeneity 7/ can also effectively decrease the upper bound of the generalization error. However, indiscriminately increasing samples may lead to an increase redundant samples from local distributions. This, in turn, can cause higher redundancy heterogeneity 7), thereby amplifying the third term and degrading overall generalization performance.
These thcoretical insights motivate us to reshape the sample distribution from the perspectives of increasing content diversity (increasing m) and balancing sample density (decreasing 17) to improve the generalization performance of BIQA.
3.3 SynDR-IQA Framework
Building upon the theoretical insights, propose the SynDR-IQA framework shown in Fig. 2. which consists of two primary strategies: distribution-aware diverse content upsampling and density-aware redundant cluster downsampling. These strategies collectively aim to reshape the synthetic data distribution to obtain more generalizable BIQA models.
3.3.1 Distribution-aware Diverse Content Upsampling
Our DDCUp strategy aims enrich training data by incorporating diverse visual content while maintaining the original content distribution.
Algorithm Distribution-aware Diverse Content Upsampling Strategy
Require: Training dataset D, Training reference set D,, Candidate reference set De, Feature extractor f(-), Distance metric Dist(-)
1: Initialize D.ew +-
2: DistT {Dist( (x+), (x+) E D,
3: for each Ie De do
4: DistC t- Dist(ff(za)) (D,))
5: if Min( DistC) > Median( Dis(T) and Max(DistC)) < Max(Dis(7) then
6:
DistN Dist(f((re) (x:e), ((Dnew)
7:
if Drew or Min DistN) > Median( Median(Dis(T) then
8: Drew Drew [x,}
9:
end if end if
11: end for
D' D GenSynDD D,, Dnew 13: return Upsampled dataset D
Algorithm I details the DDCUp strategy. We select additional reference images from KADIS-700k build the candidate reference set D... To avoid introducing excessive noise, limit its size
5

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to be equal to that of the training reference set. A feature extractor f(-). pre-trained on ImageNet, used to extract features for reference images. A distance metric, Dist(-) (cosine distance in our implementation), is utilized to guide the selection process. Our algorithm ensures that the selected images do not deviate excessively from the original content distribution while also being sufficiently distinct from each other (lines 5 and 7), thereby promoting diversity and preventing redundancy. A qualitative analysis in Appendix D visually demonstrates these effects.
Algorithm 2 Synthetic Data Generation
Require: Training dataset D. Training reference set D,, New reference set Drew Feature extractor f()), Distance metric Dist(-), Number of nearest neighbors k, Reference feature distance thresh- old T. Distortion algorithm GenDist(.), Number of distortion types T, Number of distortion level L
1: Initialize S 6- 0
2: for each Tnew E DDew do ((D,))
3: nns < (FNee(()) ((Dr); ); k; Dist)
(k- (k-Nestest Neighbors of knew
4: nuss < {no Dist(J (x, ) (Frnw) Dist(J (af"), (Fnew)) < Tos, nn E nms)
5: w < Soffmmx (Diit((( (xxp) ) (x rm E mnss)
6: for each L E TT,E do
(1.)) 7: Knew
(t.l) GenDDest(rae Knews t, 1), ynew + Ennenns Wanyin
(t.1)
8: DenSim < DenSSy U (Tneew (t.l)
9: end for
10:
end for
11:
return Generated synthetic dataset D GenSyn
After selecting diverse reference images, we generate corresponding distorted images and pseudo- labels to augment the training dataset. Algorithm 2 details this process. For each selected reference image, we apply the same distortion generation process used to create the training dataset. Notably, to reduce the increase of redundant samples and prevent additional label noise, only distortion intensities of levels 1, 3, and 5 are implemented. To generate reliable pseudo-labels for these newly distorted images, we leverage the assumption that similar content under the same distortion conditions should result in similar quality degradation. For each new reference image Nnews we identify its nearest neighbors (kNN) within the original training set's reference images based on the feature distances. To further enhance the reliability of the pseudo-labels, we filter these nearest neighbors, retaining only those whose feature distance to Inew is within certain threshold T (set to 0.05) (line 4). The pseudo-label for each distorted image of Knew is then calculated as a weighted average of the labels of its nearest neighbors' corresponding distorted images, where the weights are determined by the softmax of the feature distances.
3.3.2 Density-aware Redundant Cluster Downsampling
An overabundance of redundant samples bias the model, hindering its ability to generalize to unseen data. It also contributes to increased redundancy heterogeneity (n) in Theorem 3.J]. further increasing the generalization error. Therefore, we propose DRCDown strategy to mitigate the negative impact of redundant samples and reduce r7, thereby further enhancing the model's generalization performance.
Algorithm details the DRCDown strategy. Different from DDCUp, it obtains features before each training round, to ensure distortion-level redundancy reduction while making efficient use of training data. We identify pairs of similar samples based on both the distance metric Dist(-) and label distance (LI in our implementation) (line 3). It ensures that remove redundancy without discarding hard samples with tiny feature difference yet large quality difference. The cettorted-feattrr distances threshold Taf and the ground-truth distances threshold T, are set to 0.1, and 1 (for MOS values in [0, 10)., respectively. By considering both feature and label similarity, we aim to specifically target and reduce the density of high-density clusters that contribute significantly to redundancy heterogeneity.
After identifying similar sample pairs, we employ a disjoint set union (DSU) data structure to group these pairs into clusters (line 7). For each cluster whose size is greater than 2T,, we randomly downsample it to max([NN./3 T.) samples, where N,, is the original cluster size (line 10). This

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Algorithm 3 Density-aware Redundant Cluster Downsampling Strategy
Require:
Training dataset D, Feature extractor f(-), Distance metric Dist(-), Distorted-feature
distances threshold Tay, Ground-true distances threshold Tg. Threshold for minimum union size Tu
1: Initialize Ddown +- 0, Sim SairPars - 0
2: for each (E,,Yi). (zg.3g) do
3: if ( (2r)), (Irz)) Tay and 1// Uj| < T then (5)
4:
SimPairs <-
5: end if
6: end for
7: Unions +- DSUU(Simaairs) (Union disjoint sets of similar pairs)
8: for each u E Unions do
9:
if "LLnntthhu/ T. then
10: W t- frandomly select
Dasmmples among union]
11: end if
12: D down + down Uu
13: end for
14: return Downsampled dataset
D Doown
threshold Tn prevents excessive downsampling, ensuring that the downsampled dataset retains sufficient information for effective training. By selectively removing samples from over-represented regions, the DRCDown strategy effectively reduces redundancy and promotes more balanced data distribution, directly addressing the issue of high redundancy heterogeneity and thereby contributing to improved generalization performance. This reduction in 1) helps to lower the generalization error bound as established in Theorem 3.1 leading to a more robust and generalizable IQA model.
4 Experiments
4.1
Experimental Setups
Datasets and Protocols. We conduct experiments on eight IQA datasets: four synthetic distortion datasets LIVE (20). CSIQ 21). TID2013 (22), KADID-10K [4], three authentic distortion datasets
LIVEC 23 KonIQ-10k (24). BID (25,, and the dataset PIPAL (26 with both synthetic and algo- rithmic distortions. Model performance is assessed using Spearman's 's Rank Correlation Coefficient (SRCC) and Pearson's Linear Correlation Coefficient (PLCC), Both coefficients range from -1 to 1, with values closer indicating better performance.
Implementation Details. In our experiments, we use the same model architecture (ResNet-50 [5] and loss function (LlLoss) as DGQA [9]. For synthetic-to synthetic-to-authente --athhentii and tateettii-tt-llgrithmmiit settings, models are trained using distortion types selected by DGQA, while for the synthetic-to- synthetic setting, all distortion types are used. Following standard IQA protocols [27 28), we employed an 80/20 split by reference images for intra-dataset experiments, repeated ten times with median SRCC/PLCC reported. For cross-database experiments, models are trained on KADID- 10k and tested on other datasets. During training, one 224 x 224 patch randomly sampled from each image, with random horizontal flipping applied for data augmentation. The mini-batch size is set to 32, with a learning rate of 2 x 10 10-6, The Adam optimizer, with a weight decay of x 10-4 is used to optimize the model for 24 epochs. During testing, the predictions from five patches per image averaged for the final output. All experiments are implemented in PyTorch and on a server equipped with 2.1 10GHz Intel Xeon(R) CPU E---6620 v4 processor and four NVIDIA GTX 1080 Ti GPUs.
4.2 Performance Evaluation
Performance on the Synthetic-to-AAthnnti Setting, We first evaluate the generalization capability of SynDR-IQA when transferring from synthetic to authentic distortions. The results of the com- parison between SynDR-IQA and state-of-the-art methods are summarized in Table [ SynDR-IQA achieves leading performance across all three authentic datasets, with only slight underperformance (ono-- SRCC) and Q-Align (KADID-IOK~+LLVEC PLCC). Compared to the next best method, DGQA, our method improves the average SRCC and PLCC
against FreqAlign
7

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Table 1: Performance comparison the synthetie-to-authentii setting (KADID-IOK-LLIVES
KonIQ-1Ok, and BID).
LIVEC
KonIQ-100
BID
Average
Methods
SRCC
PLCC
SRCC
PLCC
SRCC
PLCC
SRCC
PLCC
RankIQA 129
0.491
0.495
0.603
0.551
0.510
0.367
0.535
0.471
DBCNN 30]
0.572
0.589
0,639
0,618
0.620
0.609
0.613
0.606
HyperIQA
0.490
0.487
0.545
0.556
0.379
0.282
0.472
0.442
MUSIQ 12
0.517
0.524
0.554
0.573
0.575
0.600
0.549
0.566
VCRNet 31
0.561
0.548
0.517
0.525
0.542
0.545
0.540
0.540
KGANet 32
0.575
0.528
CLIPIQA+
0.512
0.543
0,511
0.515
0.474
0.442
0.499
0,500
Q-Align
34
0.702
0.744
0.668
0.665
DANN 35
0.499
0.484
0.638
0.636
0.586
0.510
0.574
0.543
UCDA 16
0.382
0.358
0.496
0.501
0.348
0.391
0.408
0.417
RankDA 36
0.451
0.455
0.638
0.623
0.535
0.582
0.542
0.553
StyleAM
17
0.584
0.561
0.700
0.673
0.637
0.567
0.640
0.600
FreqAlign
18
0.618
0.588
0.748
0.721
0.674
0.708
0.680
0.673
DGQA 19
0.696
0.690
0.681
0.687
0.770
0.753
0.716
0.710
SynDR-IQN
0.713
0.714
0.727
0.735
0.788
0.764
0.743
0.737
Table 2:
Performance comparison on the setting of synthetii-to-algoritmmie (KADID-
10k -ll-roigri distortions on PIPAL).
DGQA
SynDR-IQA
Distortion Type
SRCC
PLCC
SRCC
PLCC
Traditional SR
0.4897
0.4567
0.5247 +3.50%
0.4808
+2.41%
PSNR-originater SR
0,5419
0.5404
0.5845
+4.26%
0.5680 +2.76%
with kernel mismatch
0.5810
0.5956
0.6263
44.53%
0.6342
+3.86%
GAN-baser SR Denoising
0.1629
0.1353
0.1998
+3.69%
0.1629
+2.76%
0.5393
0.5279
0.5749
+3.56%
0.5552
+2.73%
SR and Denoising Joint
0,5588
0.5193
0.5557
0.31 %
0.5023
1.70%
Average
0.4789
0.4625
0.5110 +3.21%
0.4839 +2.14%
across the three datasets by 2.7% and 2.7%. respectively. The significant improvement demonstrates the superior syn-to-real generalization capability of SynDR-IQA.
Performance on the Syntheticcto-Algoriiie cyothetic-to-alooritme setting. Table Coompresss our method with DGQA across different types of algorithmic distortions. SynDR-IQA consistently improves over DGQA for most distortion types. Notably, it outperforms over 4% SRCC for PSNR-originated SR and SR with kernel mismatch. slight decrease observed only for SR and Denoising Joint. Overall, these results indicates that SynDR-IQA effectively generalizes to algorithmic distortions, demonstrating its robustness in handling complex and unseen distortion types.
Setting. We further evaluate SynDR-IQA the
Performance on the Synthetic-to-Syntheei Setting, We also evaluate SynDR-IQA on the synthetic- to- no-synthetic setting. SRCC results are summarized in Table 3 SynDR-IQA shows superior per- formance over the baseline in both in-dataset and cross cross-dataset evaluations. On KADID- our method achieves SRCC of 0.8922, indicating better fitting the training data. In cross-datase testing, SynDR-IQA maintains higher performance, demonstrating enhanced generalization to other synthetic distortion datasets. These results confirm that approach enables the model to learn more robust and generalized feature representations.
4.3 Ablation Study
To understand the contributions of each component in SynDR-IQA we perform ablation experiments on the synthetic-to- ceathetic-to-authentie authentic setting (SRCC reported in Table Here, CD refers adding the full candidate dataset during training, CD+SEL denotes DDCUp, and DOWN signifies DRCDown.

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Table 3: Performance comparison on the synthetic-to-synthiii setting (single database evaluation on KADID-IOK, KADID-IOK -LVVE, CSIQ, and TID2013).
KADID-IOK
LIVE
CSIQ
TID2013
Methods
SRCC
PLCC
SRCC
PLCC
SRCC
PLCC
SRCC
PLCC
Baseline
0.8528
0.8526
0.9173
0.8988
0.7965
0.8017
0.7077
0.7220
SynDR-IQU
0.8922
0.8974
0.9258
0.9014
0.8069
0.8092
0.7147
0.7328
(a)
(b)
(c)
Figure 3: UMAP visualization of features extracted from LIVEC using the same model under four different training processes: (a) trained directly LIVEC, (b) trained directly KADID-IOK), (c) trained on KADID- 10k based on DGQA, and (d) trained on KADID- AADID-1Ok based on SynDR-IQA.
Effect of Candidate Dataset. Comparing a) and b), performance on KonIQ- 10k improves, but slightly decreases LIVEC and BID. This suggests that indiscriminately increasing data to enrich content diversity is not necessarily beneficial, as this may also include samples with significant distributional differences from the training dataset and new redundant samples, thereby hindering model generalization.
Effect of DDCUp. Comparing a), b), and e), DDCUp significantly improves performance. By selectively increasing diversity while maintaining data distribution, DDCUp effectively balances content diversity and distribution consistency, leading to better generalization across different datasets.
Effect of DRCDown. Comparing a) and e), implementing DRCDown alone already shows consistent improvement over the baseline. This indicates that controlling sample density addresses data redundancy and imbalance, yielding more precise and generalizable feature representations.
The combination all components (SynDR-IQA) yields the best overall performance, achieving a 2.71% average SRCC improvement over the baseline. These results demonstrate that each proposed component in SynDR-IQA contributes to a more robust and generalized model.
4.4 Visualization Analysis
implement the same model under four different training processes: (a) directly on LIVEC, (b) directly on KADID-IOK, (c) on KADID-10K based DGQA, and (d) KADID-10K based on SynDR-IQA. We then extract features from LIVEC using these models for UMAP visualization, as shown in Fig. 3 The visualization clearly shows that representations from the model trained directly on KADID-I0K 10k form distinct and scattered clusters, indicating poor gencralization to authentic distortions. The representations obtained from DGQA show improvement but remain relatively dispersed. In contrast, SynDR-IQA produces continuous and smooth feature patterns that are much closer to those obtained from the model trained directly LIVEC. This visually validates SynDR-IQA's effectiveness in bridging the synthetic-to-authenie domain gap.
5 Conclusion
In this work, we aim to address the critical challenge of limited generalization ability in BIQA models trained on synthetic datasets. Our investigation reveals a key pattern: representations learned from synthetic datasets tend to form discrete and clustered distributions, with high-quality image features

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Table 4: Ablation study of SynDR-IQA components on the synthetic-to-authenii setting. The
symbols v and indicate the inclusion of component.
Index
CD
SEL
DOWN
LIVEC
KonIQ-10k
BID
Average
a)
0.6958
0.6810
0.7696
0.7155
b)
0.6901
0.7105
0.7677
0.7228
C
0.6962
0.6887
0.7793
0.7214
V
V
0.7095
0.7107
0.7775
0.7326
e
V
0.7219
0.7119
0.7822
0.7387
D
0.7127
0.7268
0.7884
0.7426
clustering around reference images and low-quality features clustering based on distortion types. which significantly hinders generalization to authentic or unseen distortions. Motivated by this, we theoretically analyze the impact of sample diversity and redundancy on generalization error. Our theoretical insights underpin SynDR-IQA, novel framework designed to reshape synthetic data distributions to improve BIQA model generalization. SynDR-IQA employs two key strategies: I) DDCUp, which enhances content diversity while preserving the content distribution of the training dataset; 2) DRCDown, which optimizes sample distribution by reducing the density dense clusters. Comprehensive experiments across three cross-dataset settings consistently demonstrate that models trained with our SynDR-IQA framework achieve improved generalization ability.
Acknowledgments and Disclosure of Funding
This work was partially supported by the National Key Research and Development Program of China (2023YFA 1008500), the National Natural Science Foundation of China (62401420), and the Hangzhou Joint Fund of the Zhejiang Provincial Natural Science Foundation of China (LH25225F010003).
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70
20
50
20
(a) VGG-16
80
70
50
40
10
10
(b) Swin-T
Figure 4: UMAP visualizations of features learned by VGG-16 37 and Swin-T
38
KADID-IOK 4.
Both show discrete clustering patterns on synthetic distortion data.
A UMAP Visualizations across Different Backbones and Datasets
To further verify the generality of the phenomenon observed in Fig. and discussed in the Introduction, we visualize the feature distributions learned on different backbones and datasets. Fig. 4 present
the UMAP 3 projections of the learned features under various experimental conditions.
Fig. iillustrates the results of VGG-16 16 (37 and Swin Transformer Tiny (Swin-T) (38 trained on KADID- 10k [4). Both models cxhibit discrete and clustered feature structures synthetic distortions:
high-quality samples cluster by reference images, and low-quality samples cluster by distortion types.
Fig. 5 shows similar pattern for ResNet-50 (5] trained TID2013
222 confirming that this
phenomenon is consistent across synthetic datasets. In contrast, Fig. presents the UMAP results
of 5e [5] trained on authentic IQA datasets (LIVEC 23 BID 25 and KonIQ-10k (24)
where features are more continuous and smoothly distributed.
These differences stem from the data distribution itself. Synthetic distortion datasets, constrained by limited content diversity and redundant distortion combinations, tend to drive models toward over-clustered and discontinuous feature spaces. Authentic datasets possess more balanced and inherently diverse distribution, enabling models to learn more continuous, semantically coherent, and generalizable representations.
Visual Examples of KADID-10K
To further illustrate the distributional characteristics discussed in the main text, we present several representative visual examples from the KADID-10K 10k dataset (
Fig. shows part of the low-distortion images, all derived from the same reference image (IO1.png). These images exhibit extremely high visual similarity and are almost indistinguishable from their original reference. This suggests that low-intensity distortions have minimal impact on the visual appearance of the images, leading to high degree of redundancy among samples.
13

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15
10
Figure 5: UMAP visualization of ResNet-50
S trained on TID2013
22 demonstrating
consistent clustering trends across synthetic datasets.
(a) LIVEC
(b) BID
(c) KonIQ-10k
Figure 6: UMAP 3 visualizations ResNet-50 [5] trained on authentic datasets (LIVEC [2]]
BID 25). and KonIQ-10k (24). Features continuous and well-distributed, reflecting more balanced and diverse data structure.
Fig. presents part of the high high-distortion images from LADID-1OK. The images are dominated by strong and repetitive distortion-speciff patterns. can be observed that examples with the same distortion type show nearly identical artifact structures, and in some cases, even different distortion types may result in similar overall visual patterns. These patterns reveal the strong clustering effect caused by the synthetic distortion process.
These examples visually reinforce the observations discussed in the main text: low-distortion samples are visually redundant, while high-distortion samples often share highly similar, distortion-depeedent artifacts. This further demonstrates the discrete and clustered characteristics of the data distribution in synthetic distortion datasets.
C The Proof of Theorem 3.1
Proof. The key challenge in analyzing clustered data is that samples violate the i.i.d. assumption fundamental to standard generalization theory. To address this challenge, We begin by applying the triangle inequality to separate the generalization error into components:
[R()) Remp (f) < R(() Rm nal (f) + (m) Remp(f))
where (f-naaf) represents the empirical risk based on m i.idd samples drawn from the true distribu- tion D. We now bound each term. This decomposition isolates: 1) The standard i.i.d. generalization
error R(f) Rm-ia (f)l, 2) The clustering bias Rmaiid (f) - (f))
We analyze each component separately and then combine the results.
Step 1. Bounding Standard i.i.d. Generalization Error.
We define the supremum of the absolute difference between the true risk R(F) and the empirical risk based on m i.i.d. samples Rm-iia (f):
14

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101.png (ref)
101 101_01_0l.po
101 101_03_00 1000030111 03_0l.png
101_04
101 06 01.png
101 08 01.png
101 101_09_01.png
101 101_10_01 11__0l1pggggg
[01_16_01.p
101 101_17 77_0llpng
101_18 18 01.png
101_19_ 10_____ppgngg
Figure 7:
Part of low- oow-distotiion images in the KADID-10K dataset [3 using 101.png as reference.
These images exhibit extremely high visual similarity.
104, 11.01,.000 01.00.pgg
104, 104_02, 02_05,pug
104 04 00__004_55ppppg 05.005
104, 104_08, US
104_10_U5ppag 05.pag
104 24 05 pag
110. _1.001115.... 05..pgg
110. 02, 05
110 04 100004405ppmgg
110. 08, .0...
110, 10. 05..ng
110. 24 05,
121 01 05.png
121 02. 05.png
121 04 05.png
121 08 05.
121 10 05,png
121 24 05, 05.png
151_011 01__0ggngggggg
151_022 [51_02_05.pmg
151_04 151_04_08.ppg
151_00 [5__088_000 08..
151. 10_ 1$1_110_0_ppgg
151, [51_24_00,pmg
Figure 8: Part of high- high-distortion images the KADID-IOK dataset 38 The same distortions
exhibit very consistent patterns, and even different distortions may present very similar patterns.
15

---

( = sup
[(f)
Rm-iia(f))
SEF
By applying McDiarmid's inequality, with probability at least - -6/2:
log (2g(2/6)
(X) S [[+(X)]
m
Then, we upper bound the expectation using Rademacher complexity. Therefore, with probability least 1 5/2:
[ < 2 Radm (F),
where Radm (F) is the empirical Rademacher complexity based on m i.idd samples.
Step 2: Bounding the Clustering Bias We partition the dataset into cluster centers {21, I2....,, Jmm each sampled independently from local distributions D, C D. For simplic- ity, assume that the m i.i.d. cluster center samples noise-free, i.c. for each 24, the label is given by [[]
For each center 2;, we generate h; samples { Val,, (# yz2).... (rik, Vik.) }} from its local distribution D;, we define random variable Y; as the average loss over this cluster:
Y,
((((r)))ug)
Since y' [0, 1] and the loss function ((-) is L1, we have ((f(xz)) vy)) E [0, 1] and upper bound the variance of Y;
Var(Y,) S
4k.
Due to the high correlation between samples within clusters, the inter-cluster approximates Remp(f) this point, applying Bernstein's inequality with the variance bound above, with proba- bility at least 5/2,
m Rmm-isaf) - Remp(f)) Σ (Y - E[]] <
glog(2/6) 8m
2 2log
(3)
3m
where 7) E kg
Step 3. Combining Terms.
Applying the union bound the components, with probability least 1
8,
2 log(2//8)
log (2/))
1og(2/))
[R() Remp (f)l < Radm (F) +
(4)
Sm
3m
D Qualitative Analysis for Algorithm 1
To intuitively demonstrate the effect of Algorithm extracted features from the upsampled reference image set using an SaugeNet-pretrainng network (i.e., the feature extractor f() used in Algorithm IT and performed UMAP visualization, shown in Fig. g
From the overall distribution, it can observed that the newly added reference samples are mainly distributed in the boundary zones or gap regions among the original training reference samples. This
16

---

tie Safrenne
Figure 9: UMAP visualization of the reference image set after content upsampling by Algorithm
with features extracted via the MmageNet-pretraiing network f() Red dots denote the original Training reference samples, while blue dots denote the newly selected samples produced by Algorithm The left and right panels display image examples corresponding to local regions.
indicates that Algorithm D effectively expands the coverage the sample space without disrupting the original data distribution, thereby producing more diverse and balanced reference set.
The two local regions on the left and right sides of Fig. 2 further illustrate that the newly added samples exhibit reasonable differences from the original ones in terms of content and visual characteristics. This demonstrates that Algorithm distributional consistency and avoiding the introduction of redundant samples.
[ successfully increases content diversity while maintaining
E Hyperparameter Ablation Analysis
To comprehensively evaluate the effect of key hyperparameters in our proposed framework, perform series of ablation experiments under the synthetic-to-authemee setting (KADID-10K + LIVEC, KonIQ-10k, and BID).
Hyperparameter Analysis for DDCUp. The DDCUp involves two key parameters, as defined in Algorithm 2 the number of nearest neighbors k. which determines the diversity of pseudo-label references, and T,,, which controls the feature similarity threshold for selecting reliable neighbors. As shown in T when A is small, the pseudo-labels become unstable due insufficient neighbor information, while when & too large, unreliable neighbors introduce noise and slightly degrade performance. Similarly, relaxing T. from 0.05 0.1 causes small drop in performance because less reliable neighbors are included. The best results are obtained when = and Tzf = 0.05, which strike a good balance between diversity and reliability.
Table 5: Ablation analysis of k and T,, in DDCUp.
SRCC
k
Trf
LIVEC
KonIQ- 10k
BID
Ours
5
0.05
0.713
0.727
0.788
0.703
0.710
0.782
2
3
0.05
0.704
0.710
0.783
0.1
0.704
0.713
0.779
4
10
0.1
0.712
0.723
0.776
5
ALL
0.708
0.723
0.789
Hyperparameter Analysis for DRCDown. The DRCDown contains three key parameters, as described in Algorithm the quality score threshold Tgg the feature difference threshold Tar. and the minimum retained sample number T,. As shown in Table 6 T, and Tay jointly control redundancy detection process. If either T3 or Ty is too large, the model prunes similar samples too aggressively and may mistakenly remove diverse examples. Conversely, if T or Tar is too small, redundant samples are not detected effectively. The best results are obtained with T, 1
17

---

and Tuf 0.1, which yield the best performances by balancing effective redundancy reduction with sample diversity preservation.
Table 6: Ablation analysis of Tg and Taf in DRCDown.
SRCC
T
Tat
LIVEC
KonlQ-10k
BID
Ours
0.1
0.713
0.727
0.788
1
0,5
0.1
0.700
0.703
0.792
2
1.5
0.1
0.702
0.698
0.796
3
1
0.05
0.709
0.716
0.792
4
1
0.15
0.709
0.715
0.790
5
0.5
0.05
0.701
0.695
0.789
6
1.5
0.15
0.710
0.704
0.792
Table reports the results under various T. values. smaller T, results in excessive downsampling, losing critical information, while larger T, retains substantial redundancy and diminishes the benefit of DRCDown. The model performs best with Tu = 20, where redundant clusters are sufficiently compact while maintaining adequate representation diversity.
Table 7: Ablation analysis of T, in DRCDown.
SRCC
Tw
LIVEC
KonIQ-10k
BID
Ours
20
0.713
0.727
0.788
1
10
0.703
0.709
0.777
2
30
0.701
0.694
0.791
From these results, observe that the DRCDown module also demonstrates stable performance across a reasonable range of parameter values. Our final configurations (Tg = 1, Tay 0.1, T, = 20)
consistently yield the best trade-off between redundancy suppression and dataset representativeness.
Model Architecture and Pretraining Ablation
To thoroughly validate the generality and robustness of SynDR-IQA, we conduct ablation studies from two complementary perspectives: (1) the influence of different backbone architectures; and (2) the effect of distinct pretraining strategies, namely ImageNet and CLIP. These experiments collectively aim to assess whether SynDR-IQAN effectiveness depends specific model architectures or feature initialization schemes.
F1 Impact of Backbone Architectures
To examine architecture generality, we evaluate SynDR-IQA with two representative transformerbbaeed backbones: Vision Transformer (ViT-B/16) (39] and Swin Transformer Tiny (Swin-T) (38). Both backbones follow the training protocol in main experiments. The baseline corresponds to the DQGA.
As shown in Table SynDR-IQ2 consistently improves the baseline performance across all authentic datasets, achieving an average SRCC gain 7.2% with the Swin-T and 2.0% with ViT-B/16. These results indicate that SynDR-IQA effectively generalizes across diverse network architectures, confirming the robustness and versatility of our framework.
Table 8: Performance comparison between the baseline and SynDR-IQA using different imageeet- backbones under the synthetic-to-authennii setting (SRCC only).
Backbone
Method
LIVEC
KonIQ-10k
BID
Average
Baseline
0.620
0.600
0.729
0.649
Swin-T
SynDR-IQA
0.670
0.719
0.777
0.721
Baseline
0.714
0.694
0.740
0.716
ViT-B/16
SynDR-IQA
0.729
0.717
0.761
0.736
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F.2 Impact of Pretraining Strategies
Beyond architectural variations, we further investigate the impact of different pretraining strategies on model performance. Specifically, we compare ImageNet pretraining with CLIP pretraining 40 which demonstrates strong semantic-level feature learning through large-scale vision-language pretraining.
We first explore whether replacing the backbone entirely with a CLIP- CLIP-pretrained model can im- prove performance. Table (alleeecmmrrr the CLIP-pretrained ViT-B/16 with the ImageNet-pretraing ViT-B/16 as the backbone, under identical architectural and training configurations.
Table 9: Comparison of ImageNet and CLIP pretraining when used the backbone (SRCC only)
Backbone
Method
LIVEC
KonlQ-10k
BID
Average
Baseline
0.714
0.694
0.740
0.716
ViT-B/16 (ImageNet)
SynDR-IQA
0.729
0.717
0.761
0.736
Baseline
0.653
0.683
0.706
0.681
ViT-B/16 (CLIP)
SynDR-IQA 0.692
0.744
0.766
0.734
As shown in Table 9 replacing the backbone with CLIP does not yield a clear performance advantage over ImageNet pretraining. We attribute this CLIP's multimodal pretraining objective, which emphasizes high-level semantic alignment between vision and language modalities at the expense of low-level visual details that crucial for perceptual quality assessment tasks.
However, inspired by CLIP's superior semantic understanding capabilities, we further explore more refined strategy: employing CLIP solely in the DDCUp module (as f(-)), while retaining the MmageNet-pretrainge ViT-B/16 as the primary IQA backbone. This design takes advantage CLIP's superior semantic understanding in handling diverse content, while retaining the low-level visual representations in the main network necessary for accurate quality prediction.
Table 10: Performance comparison when using different encoders the DDCUp module. The main IQA backbone remains ViT-B/16 (ImageNet-pretrainee)) (SRCC only).
DDCUp Encoder
LIVEC
KonIQ-10k
BID
Average
ViT-B/16 (ImageNet)
0.729
0.717
0.761
0.736
ViT-B/16 (CLIP)
0.739
0.751
0.778
0.756
As shown in Table 10 selectively integrating CLIP within the DDCUp module leads to substantial performance improvements, achieving new state-of-the-arr results across all test datasets. We believe these gains stem from CLIP's enhanced semantic understanding, which enables more accurate content-aware reference selection during the upsampling process and further enhances the overall training effectiveness of the model.
G
Qualitative Results
To qualitatively demonstrate the effectiveness of method, showcase several representative examples from LIVEC in Io The examples span diverse scenarios with various quality scores, distortions, scenes, and content. Notably, our model, trained solely on the synthetic distortion dataset KADID-IOK, generates predictions that align well with human- ground truth scores on these real-world images, validating the effective synthetic-to-red generalization capability of our approach. Furthermore, compared to the -tate-of of-te-o--th-arr ahtt method CLIP-IQA [33,, our approach shows significantly better alignment with human perception.
H Limitations
While SynDR-IQA demonstrates significant improvements in synthetic-to-algoriimii generalization scenarios, there are still notable performance gaps with practical availability. We think the primary challenge stems from the fundamental difference between existing synthetic distortion patterns and algorithmic distortion characteristics. Current synthetic distortion datasets primarily focus
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0.458
3.022
5,211
7:143
3.315
5,397
66.778
2.066
3008
00.514
1.402
3,820
5.458
7433
9,045
1.726
4 188
8888
3.804
M 881
2.099
3. 7797
6.095
7.535
0.213
1.244
3.392
9 0,092
Qualitative results of SynDR-IQA LIVEC. For each image, the top number repre- sents the suman-annotatee ground-truth score, normalized to the range [0, 10]; the middle number represents model's predicted quality score; and the bottom number represents the quality score predicted by CLIP-IQA [33]. The ground-truth scores of these images progressively increase from left to right and from top bottom.
Figure 10:
traditional degradation types (c.g.. blur, noise, compression), which differ significantly from the complex patterns introduced by modern image processing algorithmss, especially those involving deep learning-based methods.
This limitation highlights the need for synthetic distortion generation methods that can produce synthetic distortions with algorithmic distortion and other complex characteristics while maintaining controllable image quality degradation. We believe addressing this limitation through future research will be crucial for further improving the generalization capability of BIQA models across different distortion scenarios.
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NeurIPS Paper Checklist
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Reviews:
[{"review_id": "spj3JpT4iB", "reviewer": "Reviewer_BpnP", "summary": "This paper is about improving the generalizability of no-reference image quality assessment methods that are trained on limited data. The authors provide a key observation that representations learned from synthetic data tend to present a discrete and clustered pattern, and this issue is due to the distribution of synthetic data, especially the given distortion types. To solve this problem, the paper proposes a novel framework called SynDR-IQA to reshape the synthetic data distribution. Specifically, after theoretical analysis, the framework distinctly upsample diverse content and downsample the redundant cluster to balance the distribution. The abundant cross-dataset evaluation demonstrates the impressive generalizability of the proposed SynDR-IQA.", "questions": "1. Can Figure 1 add some image samples? The Figure 1 only presents abstract discrete points, maybe providing a few corresponding image samples can make the figure more understandable, especially the images with high and low quality.\n2. How the content upsampling actually works? Does it just select additional images with related content from another dataset (KADIS-700K)? In this way, can a larger pool of reference images (maybe from diverse large-scale datasets) be used for selection?\n3. Is the content upsampling time-consuming? It seems selecting reference images from a large dataset can be hard to implement and time-consuming.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. The figure 1 well illustrates the limitation of training BIQA methods using synthetic data. The visualization on large-scale KADID-10K provided convincing evidence of the key challenge of distribution imbalance.\n2. The paper writing is clear and easy to understand. The introduction gives a clear entry into the current challenge of data distribution of synthetic data and elicits the explicit solution to reshape the distribution.\n3. Understandable theoretical analysis and extensive experimental results. The cross-dataset evaluation are based on multiple settings, including syn-to-real test.\n\nWeakness:\n1. Lack of comparison with more training data. The paper presents a potential solution to improve the generalizability of BIQA methods using distribution reshaping. However, directly using a larger training dataset can also solve the problem. The proposed SynDR-IQA is only effective if it can achieve similar performance compared to the same model trained on a much larger dataset, which means the SynDR-IQA can save a significant amount of native training data. In my opinion, an ablation experiment can be added that compares the SynDR-IQA and using a larger dataset to make the framework more convincing. Please note that it is not mandatory to complete the experiment during rebuttal since training with large dataset is very time-consuming. I recommend the authors can add this experiment in their final version.\n2. Figure 1 lacks some image samples, making it somewhat abstract. The figure 2 contains some samples that make it more vivid.\n3. No examples of content upsampling. Providing some original training set and corresponding selected reference image for upsampling can be very helpful to understand.", "was_revised": true, "final_justification": "The authors have well addressed my concern using a convincing small-scale experiment. I will modify my score to accept.", "rebuttal": "Thank you very much for your detailed review and valuable feedback. Your recognition is a great encouragement for us to continue improving this work, and the questions you raised are crucial for enhancing the quality of our paper. We will address each of your points below.\n\n---\n\n**Weakness 1: Lack of comparison with more training data**\n\nWe appreciate this insightful comment. We completely agree that a comparison with models trained on larger datasets would make our framework more convincing.\n\nFirst, we would like to clarify that, as revealed by previous work (e.g., [1]), simply increasing samples for synthetic datasets does not always lead to improved generalization. The distribution gap between synthetic and real-world data might be a more critical bottleneck than the amount of data. Our work focuses precisely on this issue, aiming to enhance model generalization by reshaping the data distribution.\n\nSecond, KADID-10k is currently the largest synthetic dataset with subjective annotations in the IQA field. This makes it difficult for us to find a larger, comparable dataset for a direct comparison.\n\nIn response to your suggestion, we conducted a new experiment. We applied our method to TID2013 [2], a dataset much smaller than KADID-10k (with only 25 reference images). Since distortion generation code is not publicly available for synthetic datasets other than KADID-10k, we selected the distortion types common to both TID2013 and KADID-10k (#1, #8, #10, #11, #16, #17) and followed the distortion level settings of TID2013 to implement them.  \nUltimately, we used a subset of distorted images from TID2013 (750 images in total) to train our model and compared its performance against a baseline model (ImageNet pre-trained ResNet-50) trained on the full KADID-10k dataset (10,025 images):\n\n|Method|Train Dataset|Labeled Images|LIVEC (SRCC/PLCC)|KonIQ-10k (SRCC/PLCC)|\n|---|---|---|---|---|\n|**Baseline**|KADID-10k|10,025|0.565 / 0.581|0.625 / 0.613|\n|**SynDR-IQA**|TID2013 (Part)|750|**0.611 / 0.609**|**0.664 / 0.693**|\n\nThe results clearly show that our SynDR-IQA method, using only approximately 7.5% of the baseline's data, significantly outperforms the baseline model on both the LIVEC and KonIQ-10k datasets. This experiment strongly demonstrates the value of our method.\n\n[1] Ponomarenko N, Ieremeiev O, Lukin V, et al. Color image database TID2013: Peculiarities and preliminary results[C]//European workshop on visual information processing (EUVIP). IEEE, 2013: 106-111.\n[2] Li A, Wu J, Liu Y, et al. Bridging the synthetic-to-authentic gap: Distortion-guided unsupervised domain adaptation for blind image quality assessment[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 28422-28431.\n\n---\n\n**Weakness 2 & Question 1: Figure 1 lacks image samples**\n\nThank you for the valuable suggestion. Adding specific image samples to the UMAP distribution plot in Fig. 1 would make it more intuitive and easier to understand.\n\nHowever, due to the NIPS rebuttal policy, we are unable to submit an updated figure at this stage. We commit to adding several representative image samples (especially high-quality and low-quality samples corresponding to the data points) to Fig. 1 in the final version of the paper to enhance its readability.\n\n---\n\n**Weakness 3: No examples of content upsampling**\n\nThis is an excellent suggestion. In fact, Figure 2 already includes partial examples of content upsampling, illustrating different scenarios in candidate reference selection: cases where a candidate is too different from the training set references (top left), too similar (bottom right), and pairs of candidates that are too similar to each other (bottom left).\n\nTo provide a clearer understanding, we will add more detailed examples in the appendix of the final paper, including:\n- Reference images from the original training set, with their corresponding distorted images and MOS values.\n- New reference images selected from the candidate set, along with their corresponding distorted images and generated pseudo-labels.\n\nFurthermore, along with the open-sourced code, we will provide all candidate set images and related information to facilitate reproduction and deeper understanding for other researchers.\n\n---\n\n**Question 2: How content upsampling works**\n\nThank you for your question regarding the details of our method. The process is as follows: We first use a baseline BIQA model (trained on KADID-10k) to filter relatively high-quality images from the KADIS-700k dataset. From these, we randomly sample a number of images equal to the number of reference images in KADID-10k to construct our candidate set for content upsampling. We then use an ImageNet pre-trained ResNet-50 to extract features from the reference images in the original training set and the images in the candidate set. Following Algorithm 1, we identify suitable new reference images from the candidate set and then generate new distorted samples and pseudo-labels for them using Algorithm 2.\n\nRegarding the question of \"why not use a larger, more diverse pool of reference images,\" this is theoretically feasible, as a larger pool could introduce greater content diversity. However, in practice, this approach faces two main challenges that could introduce more noise and degrade model performance:\n\n1. Large-scale image pools (like KADIS-700k) contain a significant number of low-quality images, which would lead to the generation of unreliable pseudo-labels.\n2. The limited content diversity in KADID-10k's reference set constrains the ability of our nearest-neighbor-based strategy to generate accurate pseudo-labels.\n\nOur current design strikes a fine balance between enriching content diversity and avoiding excessive noise. \n\nNotably, as mentioned in our response to Weakness 1 of Reviewer 1mY6, we found that models pre-trained with CLIP might facilitate the generation of higher-quality pseudo-labels, offering a potential direction for more reliably leveraging large-scale candidate sets in the future.\n\n---\n\n**Question 3: Is it time-consuming?**\n\nThank you for the valuable suggestion. The process is highly efficient. Excluding the generation of distorted images (which are pre-generated), the entire content upsampling process takes only about 14.5 seconds. Moreover, for a given backbone and training set, content upsampling is a one-time preprocessing step. Considering that this  tiny extra computation yields a significant performance gain in model generalization, we believe it is a highly efficient and valuable component of our framework.\n\n---\n\nOnce again, thank you for your valuable time and constructive feedback. We hope our responses have adequately addressed your concerns."}, {"review_id": "An5kgVQe6v", "reviewer": "Reviewer_57Bm", "summary": "This paper tackles the challenge of synthetic-to-real generalization in BIQA. The authors make a key observation that models trained on synthetic data tend to produce discrete, clustered feature representations that hinder generalization. Through theoretical analysis, they attribute this limitation to insufficient content diversity and sample redundancy. To overcome these challenges, they propose SynDR-IQA, a data-centric framework with two novel strategies (DDCup and DRCDown) that reshapes the training data distribution to improve generalization. Extensive experiments across multiple cross-dataset settings validate the framework's effectiveness.", "questions": "Please refer to the weakness.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strength\n1. The paper provides a novel and insightful perspective on why synthetic-to-real generalization fails in IQA, providing rigorous theoretical analysis of how sample diversity and redundancy affect generalization bounds.\n2. The proposed DDCup and DRCDown strategies directly address the identified clustering problems through a well-motivated, data-centric approach that requires no architectural changes.\n3. Consistent improvements across multiple cross-dataset evaluation settings demonstrate the effectiveness and broad applicability of the framework.\n4. The paper is clearly written and well-organized.\n\nWeakness\n1. The proposed DDCup and DRCDown strategies rely on several hyperparameters whose sensitivity is not thoroughly analyzed.\n2. More visualization results on other synthetic and real-world datasets should be provided to better support the main claims.\n3. The UMAP visualization for the model \"Trained directly on KADID-10k\" should also be added to Figure 3 to better illustrate the limitation of the original synthetic dataset.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your thorough review, encouraging assessment, and insightful suggestions. We are greatly inspired by your constructive feedback, which is invaluable for enhancing the quality of our paper. We have conducted targeted supplementary experiments and revised the manuscript accordingly. Our point-by-point responses are detailed below:\n\n---\n\n**Weakness 1: Hyperparameter sensitivity analysis**\n\nWe appreciate the insightful comments regarding the sensitivity of our method to hyperparameters. In response, we conducted additional ablation studies and have incorporated these analyses into the revised manuscript.\n\nFor Algorithm 2, the key hyperparameters are $k$ (the number of nearest neighbors for pseudo-label generation) and $T_{rf}$​ (the maximum allowed feature similarity difference for reliable neighbors). We tested various combinations of these parameters and summarized the SRCC results as follows:\n\n| SRCC | $k$ | $T_{rf}$ | LIVEC     | KonIQ-10k | BID       |\n| ---- | --- | -------- | --------- | --------- | --------- |\n| Ours | 5   | 0.05     | **0.713** | **0.727** | 0.788     |\n| 1    | 1   | \\        | 0.703     | 0.710     | 0.782     |\n| 2    | 3   | 0.05     | 0.704     | 0.710     | 0.783     |\n| 3    | 5   | 0.1      | 0.704     | 0.713     | 0.779     |\n| 4    | 10  | 0.1      | 0.712     | 0.723     | 0.776     |\n| 5    | ALL | \\        | 0.708     | 0.723     | **0.789** |\nFor Algorithm 3, we analyzed three key hyperparameters: $T_g$​ (quality score threshold), $T_{df}​$ (feature difference threshold), and $T_u$​ (minimum retained sample number after downsampling). The corresponding results are:\n\n| SRCC | $T_g$ | $T_{df}$ | LIVEC     | KonIQ-10k | BID       |\n| ---- | ----- | -------- | --------- | --------- | --------- |\n| Ours | 1     | 0.1      | **0.713** | **0.727** | 0.788     |\n| 1    | 0.5   | 0.1      | 0.700     | 0.703     | 0.792     |\n| 2    | 1.5   | 0.1      | 0.702     | 0.698     | **0.796** |\n| 3    | 1     | 0.05     | 0.709     | 0.716     | 0.792     |\n| 4    | 1     | 0.15     | 0.709     | 0.715     | 0.790     |\n| 5    | 0.5   | 0.05     | 0.701     | 0.695     | 0.789     |\n| 6    | 1.5   | 0.15     | 0.710     | 0.704     | 0.792     |\n\n| SRCC | $T_u$ | LIVEC     | KonIQ-10k | BID       |\n| ---- | ----- | --------- | --------- | --------- |\n| Ours | 20    | **0.713** | **0.727** | 0.788     |\n| 1    | 10    | 0.703     | 0.709     | 0.777     |\n| 2    | 30    | 0.701     | 0.694     | **0.791** |\n\n---\n**Weakness 2: More visualization results on additional datasets**\n\nThank you for this valuable suggestion. To comprehensively support our main arguments, we have generated feature distribution visualizations on additional synthetic (TID2013) and real-world (BID, KonIQ-10k) datasets. These new visual results are consistent with our conclusions. Due to NIPS rebuttal policies, we cannot submit new figures directly in the response. However, we assure you that these visualizations will be incorporated into the final version of the paper.\n\n---\n\n**Weakness 3: UMAP visualization of baseline model in Fig. 3**\n\nThank you for this excellent suggestion. As you proposed, adding a visualization for the baseline model (i.e., trained \"directly on KADID-10k\") to Fig. 3 highlights the advantages of our method. The baseline's features form distinct, scattered clusters (with an SRCC of only 0.5449), which stands in stark contrast to the continuous and smooth feature distribution achieved by our method. This addition makes the generalization advantage of our approach immediately clear. The updated figure will be included in the final paper.\n\n---\n\nThank you again for your valuable time and insightful feedback. Your comments have been instrumental in improving our work, and have significantly enhanced the rigor and clarity of our paper. We sincerely hope that our responses and revisions have fully addressed your concerns."}, {"review_id": "YnDNb5Xotc", "reviewer": "Reviewer_1mY6", "summary": "This paper addresses the generalization gap in Blind Image Quality Assessment (BIQA) between synthetic and real-world distortions. The authors propose SynDR-IQA, a framework to reshape synthetic data distributions by increasing content diversity and reducing sample redundancy. Two strategies are introduced: (1) Distribution-aware Diverse Content Upsampling (DDCUp) and (2) Density-aware Redundant Cluster Downsampling (DRCDown). A theoretical generalization bound is derived to justify the design choices. The method is evaluated on standard cross-dataset BIQA benchmarks and demonstrates improvements over prior work.", "questions": "• How sensitive is DDCUp to the threshold used for selecting pseudo-label neighbors? Would performance degrade if content distribution shifts?\n\n• Why was DRCDown not compared with other sampling strategies (e.g., k-means undersampling, core-set selection, curriculum filtering)?\n\n• Can you provide a variance analysis on key benchmark improvements?", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n- Targets a very important problem of limited generalization of synthetic datasets prevalent in BIQA\n\n- The paper provides a theoretically grounded explanation for the observed failure mode, using a generalization bound for clustered data.\n\n- The proposed techniques show measurable improvements over recent baselines on several cross-domain benchmarks.\n\n- The ablation and visualization studies support the hypothesis.\n\nWeakness\n\n- Both proposed techniques (DDCUp and DRCDown) are relatively simple data augmentation and filtering heuristics. There is little innovation in model design or learning methodology.\n\n- The pseudo-labeling strategy in DDCUp assumes that visually similar content leads to similar distortion behavior, which may not generalize beyond synthetic datasets. No evaluation is done to test this assumption under failure conditions.\n\n- What is the reason for suboptimal performance of the approach in LIVEC and KONIQ-10k datasets as shown in Table 1 ?\n\n- The paper lacks comparison of data downsampling approach with existing approaches such as clustering or data pruning baselines", "was_revised": true, "final_justification": "While most of my concerns have been addressed, I feel the novelty is limited. I will as a result stick to my original rating.", "rebuttal": "Thank you very much for your thorough review of our work and for the valuable comments you provided. Your professional feedback is extremely important for the improvement and refinement of our study. We understand your concerns and have provided detailed responses and clarifications to each point below.\n\n---\n\n**Weakness 1:  Lack of model and learning innovation**\n\nWe appreciate the reviewer's valuable comments. We agree that the implementation of DDCUp and DRCDown is fairly simple. However, we consider this simplicity a key strength of our work.\nOur main contribution is not a more complex model architecture. Instead, we are the first to provide a theoretically grounded and practical solution to the longstanding syn-to-real generalization challenge in BIQA, approached from the perspective of data distribution.\n\nOur key contributions can be summarized as follows:\n\n1. Through visualization and theoretical analysis, we first reveal the fundamental causes behind the lack of generalization in existing IQA models: insufficient content diversity and sample redundancy in the training data jointly lead to clustering in the feature space. This insight creates a clear path for future improvements.\n2. Based on this insight ,  we propose a framework to reshape data distribution with DDCUp and DRCDown, substantially enhancing model generalization. Additionally, as a data-based approach, our method requires no changes to existing model architectures and offers greater potential for practical deployment.\n\nIn summary, we believe that the true innovation of our work lies in shifting the research paradigm from a focus on model architecture to a broader consideration of data construction and optimization. We believe such a simple yet effective approach carries significant practical value and inspiration for BIQA and related research fields.\n\n---\n\n**Weakness2：The pseudo-labeling may not generalize beyond synthetic datasets**\n\n\nThank you for your valuable feedback. We would like to first clarify the core assumption underlying our approach. You have characterized it as \"visually similar content leads to similar distortion behavior,\" which may indeed cause some ambiguity.\n\nAs described in our paper, our actual assumption is: \"similar content under the same distortion conditions should result in similar quality degradation.\" We believe this assumption is reasonable as it builds upon a fundamental understanding in the IQA field: the perceptual quality of an image is jointly determined by its content and distortion. When distortion conditions are fixed, content becomes the primary factor influencing perceptual quality variations. Therefore, finding content-similar neighboring images and using their quality scores under the same distortion conditions as references to generate pseudo-labels represents a logically sound and well-grounded strategy, which is also supported by our experimental results.\n\nFurthermore, DDCUp is not designed as a universal pseudo-labeling scheme that generalizes to all unknown distortion types. Instead, it explicitly focuses on addressing the insufficient content diversity problem in existing synthetic distortion datasets, thereby improving model generalization in real-world distortion scenarios. Our entire experimental framework operates within the synthetic-to-real setting and makes no claims about extending beyond the scope of synthetic datasets.\n\n---\n\n**Weakness 3: Performance on the LIVEC and KonIQ-10k Datasets**\n\nWe appreciate the reviewer's detailed analysis of our results. We would like to clarify that our method is not \"suboptimal\" but rather demonstrates comprehensive and leading performance. In the overall evaluation across three test datasets, compared to the second-best method, our method achieves an average improvement of 0.027 in SRCC and 0.027 in PLCC, which significantly demonstrates the effectiveness of our approach.\n\nWhile we do not achieve the highest value on individual metrics for every dataset, this does not diminish our overall advantage. For example, in the KADID-10k→KonIQ-10k task, our SRCC is slightly lower than FreqAlign, but our PLCC metric surpasses it. More importantly, compared to FreqAlign, we achieves significant improvements across all three datasets with SRCC and PLCC increased by 0.063 and 0.064,  respectively.\n\n---\n\n**Weakness 4 & Question 2: Comparison with Other Downsampling Approaches**\n\nThank you for this valuable suggestion. To demonstrate the effectiveness of our DRCDown method, we have conducted additional experiments comparing it with Core-set Selection (K-center Greedy-based) method. The SRCC results are shown in the following table:\n\n| Method                       | LIVEC     | KonIQ-10k | BID       |\n| :--------------------------- | :-------- | :-------- | :-------- |\n| Baseline                     | 0.696     | 0.681     | 0.770     |\n| Core-set (ratio=0.8)         | 0.683     | 0.675     | 0.738     |\n| Core-set (ratio=0.6)         | 0.682     | 0.666     | 0.760     |\n| DRCDown                      | 0.690     | 0.711     | 0.768     |\n| DDCUp + Core-set (ratio=0.8) | 0.689     | 0.709     | 0.780     |\n| DDCUp + Core-set (ratio=0.6) | 0.697     | 0.705     | 0.769     |\n| DDCUp + DRCDown (Ours)   | **0.713** | **0.727** | **0.788** |\n\nThe results clearly show that our DRCDown method consistently achieves better performance. The main reason is that Core-set primarily focuses on maintaining sample diversity and may accidentally remove important samples that are visually similar yet have large label differences (common in synthetic IQA datasets). In contrast, DRCDown accurately identifies and removes only  truly redundant samples that are visually similar and have similar quality scores. This helps retain more informative samples for model training and avoids the loss of valuable information.\n\n---\n\n**Question 1: How sensitive the threshold used for selecting pseudo-label neighbors**\n\nWe appreciate the reviewer’s thoughtful questions regarding our method’s sensitivity to the pseudo-label threshold.\n\nTo address the first point, for Algorithm 2,  the key hyperparameters are $k$ (the number of nearest neighbors for pseudo-label generation) and $T_{rf}$​ (the maximum allowed feature similarity difference for reliable neighbors). We conducted additional ablation studies, varying $k$ and $T_{rf}$ across a broad range, and summarized the SRCC results in the table below:\n\n| SRCC | $k$ | $T_{rf}$ | LIVEC     | KonIQ-10k | BID       |\n| ---- | --- | -------- | --------- | --------- | --------- |\n| Ours | 5   | 0.05     | **0.713** | **0.727** | 0.788     |\n| 1    | 1   | \\        | 0.703     | 0.710     | 0.782     |\n| 2    | 3   | 0.05     | 0.704     | 0.710     | 0.783     |\n| 3    | 5   | 0.1      | 0.704     | 0.713     | 0.779     |\n| 4    | 10  | 0.1      | 0.712     | 0.723     | 0.776     |\n| 5    | ALL | \\        | 0.708     | 0.723     | **0.789** |\n\nAs shown, our method maintains stable and competitive performance under a variety of hyperparameter choices, indicating robustness rather than high sensitivity to these settings. This demonstrates that both $k$ and $T_{rf}$ can be selected within reasonable ranges without significant degradation in results.\n\n---\n\n**Question 3: Provide a variance analysis**\n\nThank you very much for this question. In the field of IQA, variance analysis is generally not feasible for cross-dataset evaluation settings. This is because the standard evaluation protocol for IQA uses fixed and complete datasets for training, without random data splits. This practice is also followed in recent works such as [1] and [2].\n\n[1] Li X, Gao T, et. al. Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:27808-27821.\n\n[2] Zhong Y, Wu X, et. al. Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:61747-61762.\n\n---\n\nWe sincerely hope that the above clarifications and the additional experimental results address your concerns. We believe that our work offers a novel, effective, and solid perspective for tackling the generalization challenge in BIQA, and we kindly ask you to reconsider the value of our contributions."}, {"review_id": "6eSOpHtg77", "reviewer": "Reviewer_eiCt", "summary": "The paper shows that that synthetic IQA datasets tend to form discrete, clustered feature spaces, high-quality images are clustered by reference, low-quality by distortion type, and medium-quality samples generally scatter inconsistently which undermines regression performance on authentic, real-world images. \n\nTo overcome this, the papers propose a new method called SynDR-IQA, constituting of DDCUp to selectively up-sample underrepresented content and DRCDown to prune over-dense clusters. Results show that the proposed method improves over compared methods.", "questions": "1. Effect of Encoders: More recent pre-trained vision encoders like CLIP/DINO/DINOv2/SigLIP could be tried.\n2. Please comment on the algorithmic complexity/Runtime of the proposed algorithms.\n3. The results in Table 3 could be made more convincing with addition of statistical significance results. \n\nThere are major concerns I have. Addressing these partialy or fully can help change my decision on the paper.", "limitations": "Some limitations are discussed. There are no expected potential negative societal implications of this work.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The authors identify a very important problem in the BIQA space. The authors identify synthetic distortions often cluster unnaturally in feature space and argue that can hurt regression-based IQA models.\n2. Relatively good number of experiments presented in this paper, along with some ablations. \n\nWeaknesses:\n1. Experiments use a supervised pre-trained ResNet-50 (and some expts with one Swin transformer variant). The paper does not justify why using a ImageNet pretrained network is a reliable estimate of content representation of \"real world\" images? It is strange that in 2025 the authors did not consider using more recent pre-trained vision encoders like CLIP/DINO/DINOv2/SigLIP or even self supervised imagenet pre-trained encoders like MoCO/SimCLR/JEPA/BYOL. \n2. There are inherent biases associated with using a pre-trained encoders. \n3. Algorithm complexity/Runtime : There is no discussion on algorithmic complexities or runtime requirements for Algo 1-3. Also there are no intuitions on how the algorithms are designed, especially algorithm 1. For example, the authors claim in the paper  \"original content distribution while also being sufficiently distinct from each other (lines 5 and 7),\" there seems to be missing inspiration why and how this design was reached.\n4. Some of the results reported in Table 3 show really small gains (cross database). It would be great if some sort of statistical significance test is performed to confirm of definite improvement. One option would be repeat the training for both the baseline and proposed 10 times or similar. \n5. Also the authors should also compare their method with the popular family of un/self-supervised methods developed specially for IQA like CONTRIQUE, Re-IQA, ARNIQA etc.", "was_revised": true, "final_justification": "The authors have conducted experiments that answer some of my concerns and helped me understand the advantages of the proposed method better. Considering the other reviews and responses to them, I am willing to increase my score by one point.", "rebuttal": "We sincerely appreciate your insightful and challenging comments. Your professional review precisely targeted the key aspects of our work and provided us with invaluable directions for improvement. We fully understand your concerns and have conducted extensive additional experiments and clarifications as per your suggestions. We believe these new results thoroughly address your questions and strongly demonstrate the value of our work.\n\n---\n\n**Weakness 1 & Weakness 2 & Question 1: Choice of pre-trained vision encoders**\n\nWe sincerely appreciate the reviewer’s valuable comments. In response to your suggestion, we have conducted additional experiments to compare the performance of CLIP as a pre-trained vision encoder (vit_base_patch16_clip_224.openai) with that of an ImageNet pre-trained encoder with the same architecture (vit_base_patch16).\n\n| Backbone            | Method    | LIVEC (SRCC/PLCC) | KonIQ-10k (SRCC/PLCC) | BID (SRCC/PLCC) |\n| :------------------ | :-------- | :---------------- | :-------------------- | :-------------- |\n| ViT-B/16 (ImageNet) | Baseline  | 0.714 / 0.737     | 0.694 / 0.732         | 0.740 / 0.740   |\n|                     | SynDR-IQA | 0.729 / 0.749     | 0.717 / 0.756         | 0.761 / 0.748   |\n| ViT-B/16 (CLIP)     | Baseline  | 0.653 / 0.662     | 0.683 / 0.738         | 0.706 / 0.709   |\n|                     | SynDR-IQA | 0.692 / 0.714     | 0.744 / 0.748         | 0.766 / 0.771   |\n\nThe results indicate that using CLIP as the main vision encoder does not bring a clear performance advantage. We suspect this is because CLIP’s multimodal pretraining emphasizes semantic-level alignment, which may come at the cost of low-level visual details crucial for IQA tasks.\n\nHowever, your suggestion inspired us to further explore new uses for CLIP. Leveraging its strong semantic understanding, we employed CLIP encoder only in the DDCUp module, which only focuses on content, while the IQA backbone remained ViT-B/16 pretrained on ImageNet.\n\n| Method        | DDCUp Backbone       | LIVEC (SRCC/PLCC) | KonIQ-10k (SRCC/PLCC) | BID (SRCC/PLCC)   |\n| :------------ | :------------------- | :---------------- | :-------------------- | :---------------- |\n| SynDR-IQA<br> | ViT-B/16  (ImageNet) | 0.729 / 0.749     | 0.717 / 0.756         | 0.761 / 0.748     |\n|               | ViT-B/16 (CLIP)      | **0.739 / 0.751** | **0.751 / 0.784**     | **0.778 / 0.769** |\n\nIt is clear that applying CLIP as the encoder in the DDCUp module significantly enhances performance, achieving new SOTA results. We believe this improvement is mainly due to the superior semantic understanding of CLIP, which enables more accurate selection of content-relevant reference images, thereby improving the quality of references used in upsampling and generating more reliable pseudo-labels for training.\n\nThank you again for your insightful suggestions!\n\n---\n\n**Weakness 3 & Question 2: Algorithm complexity/runtime and design inspiration**\n\nThank you for your valuable comments. Regarding algorithmic complexity and runtime, our method does introduce some additional computational overhead; however, this overhead is entirely confined to the training phase, and there is no extra cost during inference.\n\nSpecifically, excluding the generation of distorted images (which are pre-generated before training), the entire content upsampling process (DDCUp) takes only about 14.5 seconds. Moreover, for a given backbone and training set, content upsampling is a one-time preprocessing step. By adding the new training samples (pre-generated by DDCUp) into the training set and introducing DCRDown, the average per-epoch training time increases from 35.6 seconds to 55.2 seconds. Given that this additional computation results in significant improvements in model generalization, we believe this is a well-justified trade-off.\n\nRegarding the motivation behind Algorithm 1, our core principle is to **enrich the diversity of the training set while ensuring data reliability**. Thus, the algorithm is carefully designed to address two primary issues:\n1. **Redundant content:** Avoiding the selection of new samples that are overly similar either to existing samples or to one another, which would result in new redundant samples.\n2. **Noisy outliers:** Preventing the introduction of samples that are excessively different from the labeled data, which could shift the data distribution and, more importantly, violate the assumption about similar content thereby compromising the reliability of the generated pseudo-labels.\n\nTo this end, Algorithm 1 employs the following constraints:\n- `Min(DistN) > Median(DistT)` (Line 7) : Ensures that new reference images are sufficiently distinct from each other, thus reducing redundancy.\n- `Min(DistC) > Median(DistT)`(Line 5) : Ensures the new reference images are not too close to any existing reference image, avoiding overlap with the original dataset.\n- `Max(DistC) < Max(DistT)`(Line 5) : Restricts the maximum distance between any new and existing reference image, ensuring all new samples are still within an appropriate content distribution range, and thus reliable for pseudo-label generation.\n\nWe will provide more detailed explanations for these designs in the revised version of our manuscript.\n\n---\n\n**Weakness 4 & Quesstion 3: Performance gain and statistical significance results**\n\nThank you for this valuable suggestion. We understand the reviewer’s concern about verifying the reliability of performance improvements through statistical tests. First, we would like to clarify that the synthetic-to-synthetic experiments presented in Table 3 are not the core contribution of our work. Existing methods already demonstrate strong generalization capabilities on the synthetic-to-synthetic setting, and our improvements in this setting primarily serve to highlight the universality of our approach. The main contribution of our work lies in the more challenging synthetic-to-authentic transfer setup (Table 1), where the performance gains of our method are significant.\n\nFurthermore, in the field of IQA, statistical variance analysis is generally not applicable for cross-dataset evaluations. This is because the standard evaluation protocol for IQA uses fixed and complete datasets for training, without random data splits. This practice is also followed in recent works such as [1] and [2].\n\n[1] Li X, Gao T, et. al. Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:27808-27821.\n\n[2] Zhong Y, Wu X, et. al. Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference. Proceedings of the 41st International Conference on Machine Learning, in  Proceedings of Machine Learning Research 235:61747-61762.\n\n\n---\n\n**Weakness 5: Comparison with Self-supervised IQA Methods**  \n\nThank you for this valuable suggestion. Following your advice, we added comprehensive comparisons with leading self-supervised IQA methods such as CONTRIQUE and ARNIQA when trained on KADID-10k. The SRCC results are presented in the tables below. Due to the incomplete official implementation of Re-IQA, we were unable to reproduce its results.\n\n| Method           | LIVEC     | KonIQ-10k | BID       |\n| :--------------- | :-------- | :-------- | :-------- |\n| CONTRIQUE        | 0.350     | 0.419     | 0.322     |\n| ARNIQA           | 0.548     | 0.483     | 0.608     |\n| SynDR-IQA (Ours) | **0.713** | **0.727** | **0.788** |\nAs shown above, our method consistently outperforms existing SOTA self-supervised IQA methods on various public benchmarks. Additionally, we find that our SynDR-IQA trained only on synthetic data achieves performance that is comparable to or even exceeds these methods trained on authentic datasets. This strongly demonstrates the effectiveness and robustness of our framework.\n\n| Method                  | Train     | LIVEC     | KonIQ-10k |\n| ----------------------- | --------- | --------- | --------- |\n| CONTRIQUE (Reprod.)     | LIVEC     | -         | 0.657     |\n|                         | KonIQ-10k | **0.744** | -         |\n| CONTRIQUE (Orig. Paper) | LIVEC     | -         | 0.676     |\n|                         | KonIQ-10k | 0.731     | -         |\n| ARNIQA                  | LIVEC     | -         | 0.684     |\n|                         | KonIQ-10k | 0.724     | -         |\n| SynDR-IQA (Ours)        | LIVEC     | 0.713     | -         |\n|                         | KonIQ-10k | -         | **0.727** |\n\n---\n\nWe sincerely hope that the detailed responses and additional experiments above will resolve all your concerns. We have made every effort to strengthen our manuscript, and we respectfully ask you to reconsider our work in light of this new evidence. Once again, thank you very much for your valuable time and expert guidance!"}, {"review_id": "9h3eMdmPCG", "reviewer": "Reviewer_5ayS", "summary": "The authors propose a Blind Image Quality Assessment method employing a synthetic distortion to real distortion framework. The proposed method uses distribution-aware upsampling to obtain diverse reference contents, and density aware redundant cluster downsampling to reduce excessive redundant samples in the dataset. Experiments demonstrate that these strategies improve the performance (incrementally) across many IQA datasets.", "questions": "1. It is a bit confusing when authors interchangeably use synthetic dataset and synthetic distortions. Synthetic dataset can mean different things such as animation images, images generated through generative models etc., whereas in this context it primarily refers to natural images which are corrupted with synthetic distortions\n\n2. Is there a way to evaluate Algorithm 1 alone? The goal was to enrich training data by increasing distribution diversity. Can we qualitatively or quantitatively evaluate if the proposed scheme is actually doing this?\n\n3. Provide more intuitions on how Algorithm 1, 2 and 3 were designed. At a high level all these methods look like a bunch of 'if' conditions\n\n4. The authors mention \"only distortion intensities of levels 1, 3, and 5 are implemented\". It would be interesting to know if all distortion levels were included and performance was evaluated. This will also throw light on how each distortion level influences the overall quality related feature learning.", "limitations": "The authors acknowledge the limitations in generalization capability between synthetic and authentic distortions. I believe there is one more limitation which is based on the assumption \"similar content under the same distortion conditions should result in similar quality degradation\".", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The only novelty I see in this work is that it proposes a refinement methodology to improve diversity and reduce redundancy in the training dataset. This strategy (although designed in a very ad-hoc manner) does provide some performance benefits especially for cross dataset generalization.\n\nWeakness\n1. Related Work is incomplete. The authors have missed out key references which employ distortion classification for BIQA task\n\na. W. Kim, A.-D. Nguyen, S. Lee, and A. C. Bovik, “Dynamic receptive field generation for full-reference image quality assessment,” IEEE Trans. Image Process., vol. 29, pp. 4219–4231, 2020.\n\nb. Madhusudana, P. C., Birkbeck, N., Wang, Y., Adsumilli, B., and Bovik, A. C. Image quality assessment using\ncontrastive learning. IEEE Transactions on Image Processing, 31:4149–4161, 2022.\n\nc. Saha, A., Mishra, S., and Bovik, A. C. Re-iqa: Unsupervised learning for image quality assessment in the wild. In\nProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5846–5855, 2023\n\n2. I find it hard to connect the proposed method with Theorem 3.1. The conclusion from the theorem is pretty generic and there is no BIQA specific concept. That theorem is applicable to any clustering scheme.\n\n3. The Algorithm 1,2 and 3 look quite ad-hoc and not very intuitive. The authors use many heuristics in the algorithm without proper justification. For example in Algorithm 1 why minimum, maximum and median is used at different places? For algorithm 2 why distortion intensities of levels 1, 3, and 5 are used for all distortions? Shouldn't that be distortion specific?\n\n4. The performance improvement is often incremental when compared to the current state-of-the-art. Also many IQA datasets have been skipped from reporting\n\na. Z. Ying, H. Niu, P. Gupta, D. Mahajan, D. Ghadiyaram, and A. Bovik, “From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2020, pp. 3575–3585.\n\nb. Y. Fang, H. Zhu, Y. Zeng, K. Ma, and Z. Wang, “Perceptual quality assessment of smartphone photography,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2020, pp. 3677–3686.\n\n5. In the statement \"Notably, to reduce the increase of redundant samples and prevent additional label noise, only distortion intensities of levels 1, 3, and 5 are implemented.\" What exactly are these levels? These levels can have different meaning for different distortions?", "was_revised": true, "final_justification": "The authors addressed my major concerns I had raised. The responses by the authors are convincing to improve my original score. I believe the novelty aspect of this paper is a bit incremental making it suitable for \"Borderline Accept\" recommendation.", "rebuttal": "Thank you very much for taking the time to review our work in such detail. We deeply appreciate each of your concerns and take your suggestions very seriously. We believe that the following detailed responses and additional experiments will effectively address your doubts and demonstrate the value and rigor of our work.\n\n---\n\n**Weakness 1: Incomplete related work**\n\nThank you for your suggestion. Reference [a] (Kim et al., 2020) primarily addresses full-reference IQA, which is significantly different from our blind IQA setting. References [b] (Madhusudana et al., 2022) and [c] (Saha et al., 2023) both focus on self-supervised IQA,  which differs somewhat from our cross-domain setting. Nevertheless, we will appropriately discuss and cite these works in the revised Related Work section to provide a more comprehensive and balanced overview.\n\n---\n\n**Weakness 2:  Connection between Theorem 3.1 and proposed method**\n\nThank you for your comments. We agree that Theorem 3.1 is indeed general and can be applied to other tasks with similar data distribution characteristics. Our contribution is that, for the first time, we explicitly identify discrete clustering as the fundamental cause of syn-to-real generalization failure in IQA, and analyze its impact through theoretical derivation. However, while the theorem itself is universal, the translation from theory to practical solutions is task-specific. For instance, to address insufficient diversity, we leveraged the IQA prior that \"similar content under the same distortion conditions should result in similar quality degradation\" to design the DDCup module. For the issue of redundancy imbalance, we specifically considered the common occurrence of hard samples with slight feature differences but significant quality differences in synthetic IQA datasets.\n\nIn fact, your point that \"the theorem is applicable to any clustering scheme\" further highlights the value of our work: our theoretical framework and methodological insights can inspire solutions for other vision tasks that share similar data distribution properties.\n\n---\n\n**Weakness 3 (Part) & Question3: Algorithm design intuitions**\n\nWe appreciate the reviewer's valuable feedback. We would like to clarify that our proposed algorithms are not ad-hoc creations but are designed based on a deep understanding of the IQA task's intrinsic properties and are theoretically guided by Theorem 3.1 in our paper (increasing content diversity (m) and balancing sample density (by decreasing η) to enhance the generalization performance of the BIQA model). \n\n**Algorithm 1**:\nThe core principle behind Algorithm 1 is to enrich the diversity of the training set while ensuring data reliability. The algorithm is carefully designed to address two primary issues:\n1. **Redundant content:** Avoiding the selection of new samples that are overly similar either to existing samples or to one another, which would result in new redundant samples.\n2. **Noisy outliers:** Preventing the introduction of samples that are excessively different from the labeled data, which could shift the data distribution and, more importantly, violate the assumption about similar content thereby compromising the reliability of the generated pseudo-labels.\n\nTo this end, Algorithm 1 employs the following constraints:\n- `Min(DistN) > Median(DistT)` (Line 7) : Ensures that new reference images are sufficiently distinct from each other, thus reducing redundancy.\n- `Min(DistC) > Median(DistT)`(Line 5) : Ensures the new reference images are not too close to any existing reference image, avoiding overlap with the original dataset.\n- `Max(DistC) < Max(DistT)`(Line 5) : Restricts the maximum distance between any new and existing reference image, ensuring all new samples are still within an appropriate content distribution range, and thus reliable for pseudo-label generation.\n\n**Algorithm 2**:\nThe design of this algorithm is based on a fundamental principle in the IQA field: the perceptual quality of an image is jointly determined by its content and distortion. From this, we derive a key hypothesis that similar content under the same distortion conditions should result in similar quality degradation. Based on this hypothesis, our strategy is both intuitive and logical: we generate pseudo-labels by finding content-similar neighboring images and referencing their quality scores under same distortion conditions.\n\n**Algorithm 3**:\nAlgorithm 3 (DRCDown) aims to balance sample density (decreasing η) by downsampling redundant samples in synthetic IQA datasets. Specifically, it uses both feature distance and label distance (Line 3) to ensure that only samples highly similar in both visual content and quality score are removed. This approach effectively distinguishes truly redundant samples from hard samples with slight feature differences but significant quality differences (which is common in synthetic IQA datasets), thereby preserving valuable training information for the model.\n\n\n---\n\n**Weakness 3 (Part) & Weakness 5 & Question 4: Distortion level selection**\n\nAll synthetic IQA datasets provide multiple distortion strength settings for each distortion type. Specifically, in KADID-10k and KADIS-700k, each distortion type is available at 5 levels, each with specific parameter configurations. Our choice of levels 1, 3, and 5 is primarily to reduce data redundancy (see Page 4, Lines 124–130 for details) and to decrease the training cost.\n\nFollowing your suggestion, we further conducted experiments including all levels 1–5. The results are as follows:\n\n|                     | LIVEC (SRCC/PLCC) | KonIQ-10k (SRCC/PLCC) | BID (SRCC/PLCC) |\n| ------------------- | ----------------- | --------------------- | --------------- |\n| Levels 1,3,5 (Ours) | **0.713/0.705**   | **0.727/0.722**       | 0.788/0.769     |\n| Levels 1-5 (Full)   | 0.697/0.705       | 0.702/0.717           | **0.792/0.771** |\n\nThe results show that including all levels leads to a slight improvement on BID, but causes a performance drop on LIVEC and KonIQ-10k. This confirms that introducing excessive samples with similar distortion strength increases redundancy and can harm the model's generalization ability.\n\n---\n\n**Weakness 4: Performance improvements and dataset coverage**\n\nThank you for your comments. Our method achieves notable improvements on three real-distortion datasets for cross-domain generalization from synthetic to authentic distortion, as reported in Table 1. Specifically, on average, our approach improves SRCC by 0.027 and PLCC by 0.027 compared to the second-best method.\n\nWith respect to the additional datasets mentioned by the reviewer ([a] PaQ-2-PiQ and [b] SPAQ), we note that, to the best of our knowledge, existing methods rarely report cross-domain results (synthetic→authentic) on these datasets. For this reason, we did not originally include them in our evaluation. Nevertheless, we have now conducted additional experiments on SPAQ and compared our results against the SOTA method DGQA (reproduced):\n\n| Method            | SPAQ (SRCC/PLCC) |\n| ----------------- | ---------------- |\n| DGQA (reproduced) | 0.787/0.777      |\n| SynDR-IQA (Ours)  | **0.809/0.801**  |\nThese results on SPAQ further confirm the consistent advantage of our method.\n\n\n---\n\n**Q1: Terminology confusion**\n\nThank you for your comment. In the IQA field, the term \"synthetic dataset\" commonly refers to IQA datasets generated using synthetic distortions, as seen in related literature [1], [2]. However, to avoid confusion, we will consistently use \"synthetic IQA dataset\" in the revised manuscript for clarity.\n\n[1] Shi J, Gao P, Qin J. Transformer-based no-reference image quality assessment via supervised contrastive learning[C]//Proceedings of the AAAI conference on artificial intelligence. 2024, 38(5): 4829-4837.\n\n[2] Yang J, Fu J, Zhang Z, et al. Align-IQA: aligning image quality assessment models with diverse human preferences via customizable guidance[C]//Proceedings of the 32nd ACM International Conference on Multimedia. 2024: 10008-10017.\n\n---\n\n**Q2: Evaluating Algorithm 1**  \n\nThank you for this insightful suggestion. In the revised version, we will include UMAP visualizations and representative example images in the appendix to qualitatively show how Algorithm 1 enriches the training data and increases distributional diversity. Due to NeurIPS rebuttal policies, we are currently unable to provide these visualizations and examples at this stage, but we will add them in the final version.\n\n\n---\n\nWe sincerely hope that these detailed explanations and additional experiments can address your concerns. While there is still room for improvement in some aspects of our work, we firmly believe in its value and contribution to addressing the important issue of syn-to-real generalization in the IQA field. We kindly ask you to reconsider our work based on these clarifications, and we greatly appreciate your constructive feedback."}]

Author rebuttal:

Meta-review: This paper addresses the common synthetic-to-real generalization issue in BIQA by identifying a key observation: synthetic datasets tend to induce clustered and discrete feature distributions, which hinder model generalization. To solve this, the authors propose SynDR-IQA, a theoretically grounded data reshaping framework that combines content upsampling and redundant cluster downsampling to improve training data distribution. The method consistently improves cross-dataset performance.

While some reviewers were concerned about the novelty of the approach, the authors clarified that their primary contribution lies not in model architecture design but in providing a theoretically justified and data-centric perspective on the root causes of generalization failure in BIQA. This shift in focus opens new research directions for the field. Additionally, they provided new experiments, including outperforming baselines with less training data and leveraging CLIP encoders to enhance content selection, that demonstrate the method’s practical effectiveness and extensibility. Most reviewers responded positively following the rebuttal. Considering the importance of the problem and the novelty of the analysis perspective, the AC recommends accepting this paper.
Decision: Accept (poster)