Title: Reliable Image Quality Evaluation and Mitigating Quality Bias in Generative Models
Venue: NeurIPS 2025
Accepted: no
Keywords: Image Generation

Image Quality

Fairness

Reliability
Abstract: Discrepancies in generation quality across demographic groups pose a substantial and critical challenge in image generative models. However, the Fréchet Inception Distance (FID) score, which is widely used as an image quality evaluation metric for generative models, introduces unintended bias when assessing quality across sensitive attributes. This undermines the reliability of the evaluation procedure. This paper addresses this limitation by introducing the Difference in Quality Assessment (DQA) score, a novel approach that quantifies the reliability of existing evaluation metrics, e.g. FID. DQA assesses discrepancies in evaluated quality across demographic groups under strictly controlled conditions to effectively gauge metric reliability. Our findings reveal that traditional quality evaluation metrics can yield biased assessments across groups due to inappropriate reference set selection and inherent biases in image encoder in FID. Furthermore, we propose DQA-Guidance within diffusion model sampling to reduce quality disparities across groups. Experimental results demonstrate the utility of the DQA score in identifying biased evaluation metrics and present effective strategies to mitigate these biases. This work contributes to the development of reliable and fair evaluation metrics for generative models and provides actionable methods to address quality disparities in image generation across groups.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an evaluation score to quantify the reliability of existing evaluation metrics for image quality assessment in the scenario of quality discrepancy given certain protected attributes (e.g., gender, race, etc.). This score called DQA mainly focuses on evaluating the bias in image encoders. Authors also use this score to guide the sampling process in text-to-image generative models, in order to generate images that have less quality discrepancy given protected attributes.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. This paper has nice amount of analysis and experiments, including synthetic distribution analysis, image encoder analysis, and guidance generation, etc.\n2. This paper tested several image encoders with different architectures.\n\nWeaknesses:\n\n1. Unclear definition of quality bias. The quality bias is lack of definition in the paper. There are several different understandings presented in the paper. A) The quality of generated images given different genders should be equal; B) The quality shift of generated images given different genders from reference images given different genders should be equal. These two definitions are not the same and lead to different consequences. In the experiments, it seems that the authors adopted the B) definition. They use the images generated from T2I models (baseline) as reference images for which the qualities may not be the same across protected attributes (e.g., genders). The goal is to ensure the same quality shift across protected attributes. I do not understand why should we care about the quality shift to be the same.\n2. The evaluation dataset construction is not convincing. Generating images using the same degraded sampling procedure does not mean that the generated images will exhibit the same level of image quality degradation across protected attributes. To ensure a same level of degradation, adding the same level of noise / distortion / color jittering / blurry could be a better option.\n3. The determination of which group the generated images belong to may include biases. The authors categorize the group for generated images based on the prompt used for generation. However, it is well known that the text to image models cannot faithfully follow the prompts. Thus, the generated image may have a different gender compared to the prompt. Adding some consistency scores to make sure that the generated images follow the prompt may help.\n4. In addition, the synthetic data analysis proving the unreliability of FID also heavily rely on this miscategorization assumption (point 3 above). As shown in Fig. 3, Group B is shift toward Group A that could be understand as the generated images (shifted distribution) has a different gender than the referenced one. Using this miscategorized group to obtain group-wise quality discrepancy does not align with the reality.\n5. The setting of guidance experiments has some concerning parts. It seems that the authors use the same reference set for guidance and evaluation. This is not surprising that the metrics will be improved by using the gradient from the metric as a guidance. It would be better to use some tailored dataset, e.g., fairface, as the reference set for evaluation. It would also be interesting to see some other metrics for quality measurement, e.g., aesthetic score.\n\nQUESTIONS:\nBesides the questions mentioned in weakness, I have some follow up questions.\n\n1. As I mentioned in weakness item 1, why should we care about the quality shift to be the same instead of the quality across protected attributes to be the same?\n2. In Figure 4 b), where comes from the real images? What's the criteria to determine \"well-generated\" and \"poor-generated\"? Further, \"reference\" in Figure 4 refers to real images, however, \"reference\" refers to baseline model generated images in experiments. This makes some confusions to understand the paper. What are the poor-generated images in the blue circle? Are these images perceptually similar to the other perceivable gender?\n3. Could the authors double check the values in Figure 2 and other related figures and tables? In general, FD with DINO features will be larger than Inception features [1-2]. However, it is not the case in this manuscript.\n\n[1] Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models https://arxiv.org/abs/2306.04675\n\n[2] Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples https://arxiv.org/pdf/2302.04440\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aim at addressing quality bias in image generative models (e.g., diffusion models), where certain demographic groups (gender, race) receive systematically lower-quality outputs. It identifies two flaws in standard metrics like FID: \n\na) Inappropriate reference sets: Combined references mask group-specific quality gap;\n\n b) Biased encoders: Models like InceptionV3/CLIP produce inconsistent embeddings across groups. \n\nTo solve this, the authors propose DQA (Difference in Quality Assessment), a metric quantifying an encoder's reliability in fairly assessing quality across quality-comparable groups. Moreover, the DQA-Guidance, a sampling-time method for diffusion models that uses DQA gradients to reduce quality disparities without retraining is proposed.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n**[S1**] A meaningful work to systematically diagnose and mitigate quality bias in generative models.\n\n**[S2]** DQA isolates encoder bias via controlled datasets and relative group comparisons, and the DQA-Guidance integrates fairness into diffusion sampling, enhancing quality while reducing bias.\n\n**[S3]**  The experiments are solid and methodologically rigorous.\n\n**Weakness:**\n\n**[W1]** Ambiguity in Image Quality Definitions. The criteria for categorizing images as ``well-generate`` or ``poorly-generated`` remain somewhat vague. It is unclear whether quality distinctions derive from human evaluation or are solely determined by hyperparameter adjustments in the generative model. Crucially, the methodology for ensuring strict quality equivalence between images across demographic groups—a foundational premise for DQA—requires further elaboration. Besides, terms such as ``comparable quality`` (Line 154) and ``consistent quality`` (Line 160) similarly lack precise operational definitions.\n\n**[W2]** Rationale for DQA Term. \n\n**[W2.1]** The group-specific disparity metric,  $D(f(A_{gen}), f(A_{ref})) − D(f(B_{gen}), f(B_{ref}))$, effectively isolates encoder-induced bias when true quality is matched across groups. However, as noted (Line 137), the embedding of poorly-generated images might within in-distribution regions of that of reference images. If both poorly-generated $f(A_{gen})$ and $f(B_{gen})$ exhibit similar proximity to their references, the numerator of DQA approaches zero. Consequently, DQA may fail to detect evaluation bias in such cases. Could the authors provide insights into this?\n\n**[W2.2]** The inclusion of the denominator $D(f(I_{gen}), f(I_{ref}))$ (global distribution shift) warrants clarification: How does this term improve sensitivity to relative bias versus absolute differences? Could case analyses illustrate scenarios where the denominator adjusts misinterpretations (e.g., when overall low quality masks group-wise disparities)?\n\n\n**[W3]** The study employs external encoders (e.g., DINO-RN50) to compute DQA, then applies this to enhance diffusion models. However, an alternative approach might leverage the generative model's internal representations (e.g., tokens, latent features) directly for bias quantification. \n\n**[W4]** Limited Human Evaluation. Relies on proxy metrics (MMD/DQA), while ``human perception'' of quality fairness is not directly validated. \n\n**[W5]** Scope Constraints. This paper primary focuses on gender/race/profession; other attributes (age, disability) seems unexplored.\n\nQUESTIONS:\nplease refer to the weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe FID score is a commonly used metric for evaluating image quality in generative models, but it raises concerns about the reliability of the evaluation process. This paper proposes a novel approach called the Difference in Quality Assessment (DQA) score, which quantifies the reliability of existing evaluation metrics. The authors argue that a lower DQA score indicates a more reliable metric, similar to FD assessment.\n\nNext, they introduce DQA guidance for diffusion models, incorporating two parameters, lambda 1 and lambda 2, which regulate DQA guidance and quality regularization. The authors conduct experiments using stable diffusion alongside the proposed DQA guidance and report significant improvements in image quality, in addition to an enhanced fairness evaluation metric.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The argument that the FID score is not always useful is valid. While some works have made this claim, this paper goes further by asserting that FID is not a valid metric for evaluating fairness.\n- The use of DQA (Diversity Quality Assessment) to evaluate quality bias across different demographic groups is noteworthy.\n- This paper is pioneering in its examination of fairness in image generation quality, which is of significant interest to the community.\n- The claims regarding the bias in the image encoder used for evaluation are valid, as are the metrics employed to measure any shifts in this bias.\n\n**Weaknesses**\n\n- The paper lacks sufficient experimental evidence. Since it's an empirical study, I am particularly interested in whether the DQA guidance sampling can be applied to other methods. Could this framework be utilized with different diffusion models to enhance the sampling process? Despite the interesting direction of the paper, the lack of sufficient experimental data makes it challenging to form a convincing argument and reach solid conclusions. \n\n- The lines in the methodology section (lines 96-101) are repetitive compared to the introduction.\n\n- I recommend using consistent notations in both Section 3.1 and Figure 3.\n\n- I agree with the authors' statement in line 126 that D(A_gen, I_ref) - D(B_gen, I_ref) seems invalid. Could the authors provide more clarification on why this metric has been employed in previous works?\n\n- In Figure 4, it’s helpful to see both high-quality and low-quality images. However, I suggest clearly separating the images produced by each encoder, as this would support the arguments made in Section 3.2. Additionally, it may be beneficial to include other encoders that are fine-tuned with CLIP to mitigate bias.\n\n- Regarding the evaluation dataset, I highly recommend that the authors provide detailed information on how to regenerate the dataset, including a few samples for each degradation type and the combinations of profession, gender, and race.\n\n- It seems that λ1 and λ2 are hyperparameters that have been empirically fixed to improve the score.\n\n- For all tables and figures, it would be helpful for the authors to indicate whether a lower or higher value for the used metrics is preferable.\n\nQUESTIONS:\n- How can the lambda values be determined? Are they purely experimental and evidence-based? While I understand it may be challenging to estimate these values, I would appreciate further insight into this process.\n\n- What are the failure cases for Equation 1? When image quality is degraded, it penalizes the numerator even if the image addresses the bias, leading to a lower score. This ideally should not be the case, especially for generative models that are not as stable as diffusion or those fine-tuned on a small dataset, where image quality can be subpar. Therefore, I recommend considering a weighted average for the denominator.\n\nLIMITATIONS:\nThe DQA metric should be validated extensively before it serves as the new metric to study fairness in image generation", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper analyzes and finds demographic biases in current image quality evaluation strategies due to the selection of the reference dataset and due to biases in the image encoders. To better quantify such biases, the paper introduces the difference in quality assessment (DQA) score and evaluates various backbones. This score is subsequently applied to guide a diffusion model, which mitigates the demographic biases and improves the image quality of the generated images.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths \n\nWhile distributional fairness has been studied in the past, fairness is underexplored. The paper points out that evaluation metrics are heavily influenced by the data biases, which results in unfair evaluation metrics, which is a notable finding. It has impact on the image quality across these groups, eventually also impacting the quality of models that are trained using generated images. Code is provided for reproducability and limitations are transparently discussed in the main paper.\n\n\nWeaknesses\n\n1. The title frames to improve the reliability of image quality evaluation, which extends fairness. I would tend to preplace this adjective to more specifically describe fairness.\n2. Generally speaking, this approach is applicable to any condition that occurs less often in the training dataset of the image encoder, which goes beyond race or demographics. Framing the paper in a broader way would maybe allow a more general statement regarding the reliability of evaluations.\n3. The analysis of biases of various encoders presents a significant part of the paper. More elaborately working out the relation to previous works that discussed biases (such as (Qizhou Wang et al., Do CLIP Models Always Generalize Better than ImageNet Models?) for biases in CLIP) would be helpful to more clearly state the new findings of the work compared to previous analyses.\n4. The motivation clearly relies on the data generation for training a model in the medical domain and is referenced multiple times. However, all the related experiments are in the supplementary. The main paper should either rely less on referencing this part of the work or integrate it in the main paper. The authors should clarify its importance in the rebuttal.\n5. Regarding the experiments: The image quality is deteriorated via different strategies. A more systematic variation of one parameter would allow computing the sensitivity with respect to a specific factor that reduces image quality.\n6. Compute resources are listed but time etc. is not listed although the Answer is [Yes].\n7. The controlled dataset for the DQA evaluation is not deteriorated in a controlled manner, ie, the generated artifacts are not clear. An alternative would be also to collect a dataset from real world samples (by filtering text labels from LAION for example and applying stronger and stronger corruptions). This would have enabled a more controlled dataset. To improve the quality of the base images, standard image quality filtering approaches could have been applied.\n\nQUESTIONS:\n1. Why is MMD / FD for A larger than for B? In the visualization, it does not seem to be the case, as both points A and B should distribute rather equally or am I wrong. An intuitive explanation would help me understanding this.\n2. Regarding the DQA metric: Why is the metric normalized with the reference and not for example with A+B? This mixed overall distribution quality and fairness am I wrong? A clarification of this would be helpful.\n3. The appendix also includes the analysis when lambda1 is set to 0. However, this plot is missing the global quality metric. The case of lambda1=0 should be presented in Tab. 1 as well in my opinion since it better disentangles the impact of the fairness consideration (and not just guiding with FID score). \n4. To prevent the regularization term, an alternative would be to only use the nominator of the DQA metric as the guidance term, no? Maybe, the authors could run such an experiment or explain why this would not be reasonable.\n5. Do the authors have any thoughts to what extent the training data of the text encoder or the training data of the diffusion model explain the missing fairness?\n6. According to paper, more works addressed distribution fairness. Is the paper the first that discusses “fairness in generation quality” or the first that discusses “fairness in generation quality across demographic groups” (l. 24).\n7. Why does setting lambda1 result in excessive noise (l.601), because of the potentially larger denominator?\n8. Did the authors ablate the effect of the image encoder for the DQA guidance? How would the image quality gain deteriorate if a less reliable encoder would be used? Related to this: How much do the DQA scores differ for different attributes? Do the average rankings typically also represent the rankings for individual rankings.\n9. The work finds DINO-RN50 to perform the best? What was the used training dataset for this? How does a standard DINOv2 model (maybe with different model sizes) compare to it?\n\nI consider increasing my score\n- if the authors can convincingly discuss that their fairness evaluations are indeed a new perspective and could be straight-forwardly applied to also evaluate the impact of other biases for image quality metrics,\n- if the authors discuss the denominator or potentially run experiments without the denominator,\n- if the authors also present the main results for lambda1=0,\n- if the authors discuss the image quality of the controlled dataset using a collection of recent image quality datasets,\n- if the authors also add the standard DINOv2 model in the comparisons.\n\nLIMITATIONS:\nYes, discussed in the main paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}