Results
Eval Dataset FiveK-Target Random-Target Method PSNR \uparrow SSIM \uparrow LPIPS \downarrow Queries \downarrow PSNR \uparrow SSIM \uparrow LPIPS \downarrow Queries \downarrow CMAES hansen2006cma ; mosleh2020hardware 28.53 0.9586 0.0968 200 32.29 0.9754 0.0827 200 Monolithic Proxy tseng2019hyperparameter 21.71 0.9104 0.2144 - 21.08 0.9251 0.2785 - Cascaded Proxy tseng2022neural 22.31 0.9115 0.1939 - 21.40 0.9213 0.2613 - Ours 35.89 0.9764 0.0305 10 38.46 0.9814 0.0128 10

Results of photo finishing tuning. To evaluate the efficacy of our framework on the photo finishing tuning Task, we conduct two experiments on the FiveK validation datasets, using both expert-C targets and random targets. Our method is compared against the monolithic proxy-based approach tseng2019hyperparameter , the cascaded proxy-based method tseng2022neural , and the search-based method proposed mosleh2020hardware . Consistent with tseng2022neural , we utilize 100 iterations during inference for the proxy-based methods. Additionally, we record the number of times that the photo-finishing pipeline was queried in both search-based methods and our approach.

As illustrated in Tab. 1 , our method outperforms the others across all metrics on both FiveK-Target and Random-Target. The monolithic proxy-based method struggles with accurately representing the complex image processing pipeline, leading to suboptimal performance. While the cascaded proxy method can incrementally enhance tuning performance over its monolithic counterpart, it suffers from accumulated errors and difficulties in approximating certain operations, such as texture, resulting in poorer performance. Notably, our method only requires querying 10 times image processing pipeline, whereas the performance of the search-based method, even with 200 queries, remained inferior to ours. Further, the visualization results depicted in Fig. 4 demonstrate that our method produces visual outcomes that most closely match the target images, particularly in terms of color and brightness, when compared to all other methods. More visualization can be found in Fig. 7 of Appendix A.1 .

Efficiency. To evaluate the efficiency of our approach, we conducted speed testing experiments on a system equipped with an AMD EPYC 7402 (48C) @ 2.8 GHz CPU, 8 NVIDIA RTX 4090 GPUs with 24GB of RAM each, 512 GB of memory, and running CentOS 7.9. In line with tseng2022neural , we applied 200 iterations during inference for both the proxy-based methods tseng2019hyperparameter ; tseng2022neural and the search-based method mosleh2020hardware . We measured the execution time for each method across four different input resolutions.

As indicated in Tab. 2 , our method demonstrated superior efficiency, requiring only 1.23 seconds per execution with 4K input. While the monolithic proxy-based method outperformed the search-based method in terms of speed, it faced limitations as input resolutions increased, leading to out-of-memory (OOM) errors once GPU memory was exceeded. The cascaded proxy-based method, which includes MLP networks, was the slowest and most prone to memory overflows due to its intensive memory demands. The search-based method primarily depends on CPU performance. Despite being executed on a high-performance server, the CMAES method mosleh2020hardware requires 144 seconds to process a 4K input image, which is considerably slow.

Results of photo stylization tuning. We conduct qualitative comparisons and user studies on the photo stylization tuning task to demonstrate the effectiveness of our goal-conditioned RL framework. Our policy was trained using the FiveK training dataset, and all methods were evaluated on input and style image pairs collected from the Adobe Lightroom Discover website. For all baseline methods, we adopt the same style score described Sec. 3.4 as optimization objectives, ensuring fair comparison.

As shown in Fig. 4 , our method produces results closer to the style goal image compared to the baseline. Notably, our method achieves better results with only 10 queries to the image processing pipeline, whereas the baseline methods require 200 queries, making them orders of magnitude slower.
It is important to note that the style images from the Lightroom Discover website have a different distribution than our training dataset. Despite this, our method adapts directly to the target style image distribution during testing, whereas the baseline methods require time-consuming optimization of testing data. These experimental results demonstrate that our RL-based framework can efficiently tune images to different unseen styles, showcasing that our photo finishing state representation (Sec. 3.3 ) has the capability to generalize to versatile goals outside of training distributions. More visualization with versatile goal images can be found in Fig. 8 of Appendix A.2 .

To rigorously evaluate the effectiveness of our method in photo stylization tuning, we implemented a subjective user study comprising 20 questions. In each question, participants were presented with images generated by four different methods—monolithic proxy, cascaded proxy, CMAES, and our own approach. Participants were asked to identify up to two images that most closely resembled a given target image. The study was conducted online, garnering 65 responses from a diverse group of individuals selected randomly from the internet.

The aggregated preferences are visually summarized in Fig. 6 .
The data clearly show that our method is perceived by the majority of participants as producing results that most closely match the target images, highlighting its superiority in stylization tuning tasks.

Cross dataset generalization. We conducted additional evaluations using the HDR+ dataset hdrplus to demonstrate our RL-based framework’s ability to generalize effectively to unseen datasets. We compare to baselines including CMAES hansen2006cma ; mosleh2020hardware , Cascaded Proxy tseng2022neural , Monolithic Proxy tseng2019hyperparameter , and Greedy Search Kim2023LearningCI . In Tab. 3 , we report PSNR, SSIM, LPIPS, and queries to the ISP pipeline.
The results demonstrate that our RL policy generalizes effectively to unseen data, achieving higher photo-finishing quality than methods directly tuned on the test dataset. Qualitative comparisons in Fig. 5 show that our results are closer to targets, even with input and target images outside the training distribution.

Eval Dataset HDR+ Target Method PSNR \uparrow SSIM \uparrow LPIPS \downarrow Queries \downarrow CMAES hansen2006cma ; mosleh2020hardware 28.08 0.9539 0.1307 200 Greedy Search Kim2023LearningCI 25.79 0.9212 0.1542 200 Monolithic Proxy tseng2019hyperparameter 17.80 0.8940 0.3044 - Cascaded Proxy tseng2022neural 18.90 0.8982 0.2797 - Ours 31.54 0.9652 0.0563 10

In Tab. 3 , our RL-based method achieves a PSNR of 31.54 on the HDR+ photo-finishing task, and it outperforms all baselines. This shows that our RL policy, trained on the FiveK dataset, effectively generalizes to the HDR+ dataset. Such out-of-distribution capability is facilitated by our proposed photo-finishing state representation, which extracts invariant features for photo finishing, allowing adaptation to diverse inputs and goals beyond the training distributions.
The CMAES hansen2006cma ; mosleh2020hardware baseline shows consistent results on HDR+ compared to FiveK, as it is directly optimized on the test dataset without prior training. However, proxy-based methods tseng2019hyperparameter ; tseng2022neural perform worse because the proxy network trained on FiveK does not generalize well to HDR+, leading to incorrect gradients and poorer photo-finishing quality.