MLP Embedded Inverse Tone Mapping

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-25 11:28:09

Abstract: The advent of High Dynamic Range/Wide Color Gamut (HDR/WCG) display technology has made significant progress in providing exceptional richness and vibrancy for the human visual experience. However, the widespread adoption of HDR/WCG images is hindered by their substantial storage requirements, imposing significant bandwidth challenges during distribution. Besides, HDR/WCG images are often tone-mapped into Standard Dynamic Range (SDR) versions for compatibility, necessitating the usage of inverse Tone Mapping (iTM) techniques to reconstruct their original representation. In this work, we propose a meta-transfer learning framework for practical HDR/WCG media transmission by embedding image-wise metadata into their SDR counterparts for later iTM reconstruction. Specifically, we devise a meta-learning strategy to pre-train a lightweight multilayer perceptron (MLP) model that maps SDR pixels to HDR/WCG ones on an external dataset, resulting in a domain-wise iTM model. Subsequently, for the transfer learning process of each HDR/WCG image, we present a spatial-aware online mining mechanism to select challenging training pairs to adapt the meta-trained model to an image-wise iTM model. Finally, the adapted MLP, embedded as metadata, is transmitted alongside the SDR image, facilitating the reconstruction of the original image on HDR/WCG displays. We conduct extensive experiments and evaluate the proposed framework with diverse metrics. Compared with existing solutions, our framework shows superior performance in fidelity (up to 3dB gain in perceptual-uniform PSNR), minimal latency (1.2s for adaptation and 2ms for reconstruction of a 4K image), and negligible overhead (40KB).

Main paper: 
 introduction: 
 The concept of High Dynamic Range/Wide Color Gamut (HDR/WCG) media, as defined in literature [32, 36] , encompasses an extended range of luminance representation and an augmented capability for reproducing a broader spectrum of visible colors, significantly surpassing the limitations of the Standard Dynamic Range (SDR) media definition. The advent of HDR/WCG display technology represents a noteworthy milestone in the multimedia sector, offering unparalleled richness and vibrancy in visual experiences. However, the widespread adoption of HDR/WCG technology encounters substantial challenges, primarily due to its extensive storage and bandwidth requirements during distribution [32] . For instance, as depicted in Fig. 1 (a), a typical 4K HDR/WCG media requires 20MB for storage and a bandwidth of 25Mbps for real-time streaming. At present, the concurrent usage of HDR/WCG and SDR media remains the prevailing practice. On one hand, owing to the prevalence of SDR displays, HDR/WCG contents are frequently tone-mapped (TM) to their SDR counterparts to ensure compatibility [11, 34] . On the other hand, there exists a considerable amount of SDR content that requires conversion into HDR/WCG versions for display on HDR/WCG screens [33] . Consequently, there is a substantial demand for inverse Tone Mapping (iTM) methods that aim to predict lost HDR/WCG information from SDR versions, as illustrated in Fig. 1(b) .Existing iTM methods encounter several significant challenges. Rule-based iTM methods often suffer from artifacts and inadequate detail reconstruction, particularly in poorly exposed areas [14, 34] . Learning-based methods, on the other hand, rely on an external dataset to model the iTM task as a mapping problem between SDR and HDR/WCG domains [3, 4, 7, 9, [12] [13] [14] 42] . However, these methods usually assume a predefined TM operator, which may not consistently align with SDR inputs from various sources. Furthermore, due to the inherently ill-posed nature of domain-wise iTM, these methods often require significant computational resources, thereby failing to meet the requirements for real-time presentation of high-definition HDR/WCG content.In this work, as shown in Fig. 1 (c), we present a novel metatransfer framework for the practical transmission of HDR/WCG media by embedding a learned multilayer perceptron (MLP) model as metadata into their SDR versions, where the embedded MLP is employed for image-wise iTM reconstruction. Specifically, our framework contains three stages, i.e., meta-training, transfer, and reconstruction stages. Firstly, in the meta-training stage, we take advantage of an external dataset to train a lightweight MLP model that maps SDR pixels to HDR/WCG ones. This MLP serves as a domain-wise iTM model, and we devise a meta-learning strategy to prepare it as an initialization point for fast adaptation to an image-wise one. Secondly, in the transfer stage, we utilize pixel samples from the current HDR/WCG-SDR pair to fine-tune the meta-trained MLP and embed it as metadata in the SDR image. We adopt an online hard example mining (OHEM) mechanism to select training pixel pairs according to spatial-aware feedback from the loss function, improving the adaptation performance to the current image. Finally, in the reconstruction stage, we restore the HDR/WCG content from the SDR version with the embedded MLP model for image-wise iTM.We evaluate the proposed framework with diverse metrics, including perceptual uniform, HDR-tailored, chromaticity, and conventional ones. Compared with existing solutions, our framework shows superior performance in fidelity (up to 3dB gain in perceptualuniform PSNR), minimal latency (1.2s for adaptation and 2ms for reconstruction of a 4K image), and negligible overhead (40KB). Besides, we show that our framework enjoys better generalization capacity to different TM operators and an application of our framework to accelerate domain-wise iTM methods via distillation.To summarize, our contributions are three-fold:• We propose a meta-transfer learning framework for HDR/WCG media transmission by embedding a learned image-wise iTM MLP as metadata to SDR versions. • We devise a meta-learning strategy and a spatial-aware online mining mechanism to improve the generalization and adaptation capacity of the iTM model. • Compared to existing methods, our framework exhibits superior performance, lower latency, and less overhead, showing its effectiveness and practicability. 
related works 2.1 inverse tone mapping: 
 The objective of iTM is to transform SDR images into HDR/WCG images. The traditional iTM methods mainly focus on adjusting the parameters of the global mapping function based on the image content [1, 21, 27] and employ edge enhancement in saturated regions [15] . Recently, learning-based iTM boasts superior performance compared to traditional methods. Initially, researchers [12] [13] [14] attempt to address both iTM and super-resolution jointly. Chen et al. [3] are the pioneers in treating the iTM task as an independent task and utilizing Unet [29] for end-to-end iTM. Chen et al. [4] propose HDRTVNet which consists of global color mapping, local enhancement, and highlight generation stages. Recently, Xu et al. [42] propose FMNet that incorporates frequency analysis to improve the reconstruction performance. Huang et al. [9] propose ICtCpNet to further improve the iTM performance by separating the chromaticity and luminance channels. Recently, the idea of embedding metadata into low-quality data (e.g., sRGB image) for later reconstruction of high-quality data (e.g., RAW image) has been explored in other tasks, i.e., RAW image reconstruction and color gamut expansion. Rang and Brown [26] store a set of RAW reconstruction parameters, which model the typical operations in image processing pipeline. Punnappurath and Brown [25] present an algorithm that estimates interpolation parameters in reconstruction stage, utilizing uniformly sampled RAW image pixels stored as metadata. Nam et al. [22] propose a sampling strategy that selects representative RAW pixels based on superpixels using a pre-trained Unet [29] . Another Unet is trained to reconstruct RAW images from sRGB images and sampled pixels. Li et al. [17] attempt to employ an implicit neural function to map coordinates to RAW pixels conditioned with sRGB pixels. Beyond RAW reconstruction, metadata-based techniques are also applied in the domain of color gamut expansion, as demonstrated by GamutMLP [16] . In this method, pixels in WCG (BT.2020 [32] ) but out of standard color gamut (BT.709 [2] ) are randomly sampled to train an MLP. The fine-tuned weights are embedded as metadata to assist in the reconstruction of WCG images. We introduce this idea into the iTM task. Further, we propose a meta-learning pretraining strategy and spatial-aware OHEM mechanism to further improve its performance and efficiency for image-wise iTM.Meta-learning, also known as learning to learn, facilitates rapid adaptation to new tasks. It is widely integrated in various computer vision tasks, e.g., classification [28, 30] , detection [39] , segmentation [18] , and tracking [38] . Finn et al. [6] initialize model parameters and adapt them to support sets in the inner loop. Then, they use these adapted parameters to measure the loss in query sets and finally update the original parameters with this loss, namely the outer loop. Nichol et al. [23] simplify meta-learning by directly updating meta-network parameters using the difference between original and inner updated parameters. In our framework, we treat each image's iTM task as an individual task. Different from the pre-training strategy in GamutMLP, we introduce a fast-adaptation step before the outer loop, integrating the procedure prior to the transfer stage into our meta-training stage.OHEM [35] is a methodology aimed at enhancing model performance through the selective inclusion of challenging samples. In contrast to conventional uniform sampling mechanisms, OHEM prioritizes samples that significantly contribute to enhancing model performance. Shrivastava et al. [35] first apply it in the object detection domain. After that, OHEM is widely integrated with classification tasks [5, 8] . In our framework, we adapt OHEM by introducing spatial-aware feedback from pixel-level error maps. 
mlp embedded inverse tone mapping 3.1 framework overview: 
 As shown in Fig. 2 , our framework contains three principal stages: the meta-training stage, the transfer stage, and the reconstruction stage. During the meta-training stage, we leverage an external HDR/WCG dataset and randomly sample HDR/WCG images, which are used to generate data pairs via a predefined TM operator. These data pairs are subsequently employed in a meta-learning manner to pre-train a lightweight MLP that maps SDR pixels to HDR/WCG pixels, resulting in pre-trained domain-wise weights 𝜃 𝑖𝑛𝑖𝑡 .In the transfer stage, taking advantage of pre-trained 𝜃 𝑖𝑛𝑖𝑡 , the initialized MLP 𝑀 𝜃 𝑖𝑛𝑖𝑡 is enabled to be fine-tuned from a universal MLP to an image-wise one easily. For each HDR/WCG image 𝐼 𝐻 𝐷𝑅 and SDR image 𝐼 𝑆𝐷𝑅 pair, we sample a small portion of pixels to construct the pixel sets 𝐻 𝑔𝑡 and 𝐻 𝑖𝑛 , respectively. 𝐻 𝑖𝑛 is then fed into 𝑀 𝜃 𝑖𝑛𝑖𝑡 , yielding with prediction pixel set 𝐻 𝑜𝑢𝑡 . Finally, the loss L between 𝐻 𝑔𝑡 and 𝐻 𝑜𝑢𝑡 is used to fine-tune 𝑀 𝜃 𝑖𝑛𝑖𝑡 . What's more, we adopt an OHEM mechanism to select challenging training pixel pairs according to spatial-aware feedback from the loss function, improving the adaptation performance to the current image. After adaptation on the current HDR/WCG-SDR pair, we get the imagewise weights 𝜃 𝑖𝑚 and embed them into 𝐼 𝑆𝐷𝑅 as metadata.As to the reconstruction stage, for HDRTV compatibility, we input 𝐼 𝑆𝐷𝑅 into the MLP, which is loaded with image-wise weights 𝜃 𝑖𝑚 embedded in the 𝐼 𝑆𝐷𝑅 . Consequently, the 4K HDR/WCG image 𝐼 ′ 𝐻 𝐷𝑅 can be reconstructed with high efficiency. Inspired by INF [17] , we adopt an MLP architecture where the implicit neural function mapping coordinate (𝑥, 𝑦) to pixel (𝑅 𝐻 𝐷𝑅 , 𝐺 𝐻 𝐷𝑅 , 𝐵 𝐻 𝐷𝑅 ) is conditioned by the input SDR pixel (𝑅 𝑆𝐷𝑅 , 𝐺 𝑆𝐷𝑅 , 𝐵 𝑆𝐷𝑅 ). As shown in Fig. 3 , we input the coordinate and SDR pixel into two branches respectively, resulting in coordinate features and RGB features. Then all the features are concatenated up to fusion features which finally output HDR pixels.In order to make our framework more adaptive and maximize the benefits of image-wise adaptation, we propose a meta-learning pre-training strategy, as shown in Fig. 4 . For each iteration, we construct a task by randomly sampling a support set 𝑆 𝑛 and a query set 𝑄 𝑛 from external HDR/WCG-SDR data pairs. Initially, our focus lies on facilitating the MLP's acquisition of a universal domain mapping capability from SDR pixels to HDR/WCG ones, termed a domain-wise iTM task. So we update 𝜃 𝑛 to 𝜃 ′ 𝑛 via 𝑆 𝑛 , namely the inner update. In the common meta-learning paradigm [6] , 𝜃 𝑛 is updated 𝜃 𝑛+1 directly with 𝑄 𝑛 and 𝜃 ′ 𝑛 , i.e., the outer update. But, in our framework, we aim at image-wise iTM, where the iTM model is adapted to the HDR/WCG-SDR image pair before inference. Thus, we introduce a fast-adaptation step before the outer update, integrating the procedure prior to the transfer stage into our metatrain stage. In this step, we adapt 𝜃 ′ 𝑛 to 𝑄 𝑛 and then we update the 𝜃 𝑛 to 𝜃 𝑛+1 , via the reconstruction loss calculated by applying the MLP with 𝜃 ′ 𝑛 on 𝑄 𝑛 . Finally, we can get a meta-trained MLP model 𝑀 𝜃 𝑖𝑛𝑖𝑡 , which can be easily adapted from a domain-wise iTM model to an image-wise one. We summarize this strategy in Algorithm 1.After getting the initialization weights 𝜃 𝑖𝑛𝑖𝑡 and loading it into 𝑀 𝜃 𝑖𝑛𝑖𝑡 , for each HDR/WCG-SDR image pair, we fine-tune a domainwise MLP to an image-wise one. In detail, as shown in Fig. 2 , we sample pixels from the HDR/WCG image 𝐼 𝐻 𝐷𝑅 and the SDR image 𝐼 𝑆𝐷𝑅 to construct pixel sets 𝐻 𝑖𝑛 and 𝐻 𝑔𝑡 . Then 𝐻 𝑖𝑛 and 𝐻 𝑔𝑡 are utilized to fine-tune 𝑀 𝜃 𝑖𝑛𝑖𝑡 , yielding the image-wise weights 𝜃 𝑖𝑚 , which are embedded into 𝐼 𝑆𝐷𝑅 as metadata. Previous metadata-based reconstruction methods, either uniform [17, 25] or non-uniform [16, 22] , only utilize a very limited portion of the HDR pixels (2%) in the sampling process. This limitation leads to poor reconstruction quality in hard examples, i.e., saturated regions (as shown later in Fig. 8 ). Thus, inspired by the seminal work of OHEM [35] , we propose the spatial-aware OHEM mechanism, as shown in Fig. 5 . For a certain interval during fine-tuning, we get spatial-aware feedback via applying the MLP model and obtaining an error map 𝐸, which indicates the current reconstruction performance of each pixel. Then we prioritize selecting pixels with larger errors, e.g., the pixels in the red circle of Fig. 5 . Such mechanism make our fine-tuning process more focused on poorly reconstructed regions. Our spatial-aware OHEM mechanism is summarized as Algorithm 2.As shown in Fig. 2 , the image-wise weights 𝜃 𝑖𝑚 are embedded into SDR image 𝐼 𝑆𝐷𝑅 for transmission and distribution. Our framework is naturally compatible with SDR displays. For HDRTV compatibility, we input 𝐼 𝑆𝐷𝑅 into the MLP, which is loaded with 𝜃 𝑖𝑚 . Hence, our framework not only ensures compatibility with SDRTV but also excels in the high-quality and efficient reconstruction of HDR/WCG content for seamless integration with HDRTV, all achieved within the constraints of a mere 2% increase in data. 
experiments 4.1 settings: 
 Datasets. We utilize the HDRTV4K dataset [7] to evaluate the effectiveness of our proposed framework. This dataset consists of 3,878 BT.2020/PQ1000 4K HDR images. Following previous works [4, 7, 42] , we adopt YouTube TM operator to generate data pairs. We split them according to the official split [7] into a training set and a test set, which includes 3,478 and 400 images, respectively. Evaluation metrics. Conventional image quality metrics, such as PSNR and SSIM [40] , can not be directly computed on linear high dynamic range color values because such values are nonlinearly related to the perception of visible differences [19] . Instead, in our experiments, we adopt the perceptual uniform (PU) encoder [19] to convert the absolute values of HDR/WCG images into PU values. In this PU space, we employ metrics, including PSNR, PSNR (Y), MS-SSIM [41] , feature similarity (FSIM) [44] , and visual saliency-induced index (VSI) [43] , to evaluate the performance of our framework and comparison methods. Additionally, we report the HDR-VDP3 [20] metric that is tailored for HDR/WCG image quality assessment. However, most of the metrics mentioned above operate in the luminance domain (HDR), so we introduce the chromaticity metric Δ 𝐼𝑇 𝑃 [10] to evaluate the reconstruction results in terms of chromaticity (WCG). Finally, we also report the conventional metrics, PSNR and SSIM [40] , as a reference. We report running latency and model size for efficiency evaluation. Comparison methods. We compare our framework with several state-of-the-art domain-wise iTM methods, including HDRTVNet [4] , FMNet [42] , and ICtCpNet [9] . Moreover, we include metadatabased reconstruction methods designed for other tasks, including GamutMLP [16] for color gamut expansion, CAM [22] , and INF [17] for RAW reconstruction. Implementation details. Our MLP consists of four layers and the channel numbers are 64, 32, 64, and 3. In the meta-training stage, each task consists of 3 data pairs in the support set and 1 data pair in the query set. For each task, we fine-tune the model for 500 epochs at a learning rate of 1 × 10 -4 . In the transfer stage, we resample the pixel sets in the last 25 percent of the adaptation epochs at an interval of 50 epochs. We get the official codes of domain-wise iTM methods and fully retrain their models. As to the metadata-based reconstruction methods, we make necessary modifications based on the official codes to adapt them to the iTM task. Our code is based on PyTorch [24] and will be made publicly available upon acceptance.Perceptual [4] , FMNet [42] , ICtCpNet [9] , GamutMLP [16] , INF [17] and our framework. We visualize the error maps below the results. quite well in the HDR-VDP3 and PSNR (Y) metrics, but performs poorly in the Δ 𝐼𝑇 𝑃 , which indicates the quality of chromaticity accuracy. We attribute this phenomenon to that its sampling strategy where most of the training pairs are sampled from out-of-gamut regions is not suitable for the iTM task, which requires the algorithm to model the luminance and chromaticity mapping relationship between SDR and HDR/WCG simultaneously. In addition, because CAM [22] consumes more than 64GB of GPU memory for 4K resolution, we compare our framework with CAM on a 1080p downsampled version of the HDRTV4K dataset. Our framework achieves 37.88dB, 9.529, 4.467 in PU PSNR, HDR-VDP3, and Δ 𝐼𝑇 𝑃 , while those of CAM are 34.25dB, 9.266, and 6.639. Qualitative results. We show reconstructed HDR/WCG images and error maps in Fig. 6 . Existing domain-wise methods suffer from a lack of highlighted pixels and color shifts, while metadata-based methods, especially our framework, could reconstruct the overexposure regions accurately. In Fig. 7 , for the input SDR image, the ground truth HDR/WCG image, and reconstruction results, the pixel distributions are visualized in the CIE xyY space [31] . Compared to HDR/WCG images, SDR images suffer significant loss of [4] , FMNet [42] , ICtCpNet [9] , GamutMLP [16] , INF [17] and our framework. The pixel distributions are visualized in the CIE xyY space [31] . Illustrations within the red depict the distribution in terms of chromaticity (xy), while those within the blue in terms of luminance (Y).information in both the luminance and color gamut. HDRTVNet, FMNET, and ICtCpNet have demonstrated notable performance in chromaticity domain reconstruction. However, in terms of luminance domain reconstruction, these methods exhibit shortcomings in reconstructing high-luminance pixels adequately. GamutMLP suffers from inaccurate chromaticity domain reconstruction, while INF exhibits relative weaknesses in luminance domain. More visual comparisons are available in supplementary materials.In practical HDR/WCG media transmission, latency and storage consumption are also essential metrics. As listed in Table 2 , we evaluate the efficiency of our framework. Compared with domainwise iTM methods, our framework only needs 2.13ms to reconstruct a 4K resolution image, while FMNet needs 724.10ms. Our advantage will be even more pronounced, when compared with metadatabased methods, like INF (1.22s vs. 13.46s in transfer time and 40KB vs. 2.9MB in extra data). Our framework has a comparable scale in the size of extra data compared to GamutMLP (40KB vs. 23KB) and exhibits a significant superiority of over 30 times faster in terms of reconstruction latency (2.13ms vs. 70.8ms).As shown in Table 3 , when the inference (OCIO [37] ) TM operator is different from training (YouTube), domain-wise iTM methods perform poorly, showing the limitation of domain-wise iTM methods. In comparison, metadata-based methods can reconstruct the HDR/WCG images accurately. Compared with other metadatabased methods, our framework still has a significant lead in metrics (especially in PU PSNR and Δ 𝐼𝑇 𝑃 ), demonstrating the superior generalization capacity of our framework.The effectiveness of the meta-learning strategy. We evaluate the performance of our framework with the naive pre-training 4 , our meta-learning pre-training strategy significantly improves the performance of the reconstruction. The pre-training strategy in GamutMLP [16] is similar to ours, but lacks the adaptation step before outer update, leading to inferior performance. The generalization evaluation results, shown in Table 3 , also reveal the effectiveness of the meta-training strategy.The effectiveness of the spatial-aware OHEM mechanism. We evaluate the performance of the spatial-aware OHEM mechanism and the uniform sample strategy in our framework. The results, shown in Table 4 , reveal that our spatial-aware OHEM mechanism efficiently improves the performance across all pre-training strategies. Additionally, we visualize error maps for different combinations in Fig. 8 . It can be observed that the utilization of the spatial-aware OHEM mechanism yields superior reconstruction results and overall exhibits greater uniformity. Particularly, the challenging reconstruction region within the red frame further highlights the effectiveness of the spatial-aware OHEM sample mechanism.There exists a considerable amount of SDR content that the ground truth HDR/WCG counterparts are not available. In such scenarios, Ground Truth Input SDR Figure 9: The results of iTM acceleration application. Our framework could achieve comparable performance with existing methods [42] in significantly less time.we show that our framework is capable of working with existing iTM methods for the acceleration of HDR/WCG prediction by "distilling" them from a domain-wise model to an image-wise one. Specifically, we replace the ground truth HDR/WCG image in the transfer stage with results reconstructed by existing domainwise iTM methods. Fig. 9 presents an example of the image-wise distillation results, demonstrating that our framework achieves comparable performance with significantly less latency. 
conclusion: 
 In this work, we propose a meta-transfer learning framework for HDR/WCG media transmission by embedding an image-wise iTM model as metadata in the SDR version. To improve the generalization and adaptation capacity of our framework, we introduce a meta-learning strategy and a spatial-aware online mining mechanism. Through comprehensive experiments, our framework demonstrates significant superiority over existing solutions in terms of both performance and efficiency. Furthermore, our framework also generalizes well to different TM operators and can be utilized for iTM acceleration. Our future work includes extending our framework to the video iTM task, which has higher requirements for inter-frame coherence as well as latency. 
 
 Appendix:  


Decision: All reviewers are agreed to accept the paper.

Reviews: review: paper_summary: paper_summary: The paper presents a novel inverse tone mapping method that utilizes the idea of embedding metadata in SDR images. This approach addresses the storage and bandwidth challenges of HDR images by including metadata in the SDR transmission, which is later used to reconstruct the HDR image. Although the article is well-written, employs a good evaluation methodology, and shows promising results, some improvements are needed, and some questions remain unanswered.

### General Comments:

- The approach's dependency on the tone-mapping operator used to generate the HDR-SDR data pairs for pre-training is not clearly addressed. The authors do not mention the tone-mapping operator at the beginning but do so later in Section 4. Given that their proposed method uses a spatial-aware mechanism to fine-tune the model, different TM operators might show varying levels of effectiveness. The authors should clarify this dependency and include it in their article.
- All experiments have been conducted on static images. While this is mentioned as future work, the authors should explain whether the current method is stable over time and if it is prone to temporal artifacts.
- Professional visual content often includes completely black or white frames, as well as fade-in and fade-out sequences. The authors should discuss the performance of their method in these scenarios.



### Detailed comments:
### Section 1

- **Line 168:** This is not a contribution but a feature of the proposed iTM. This sentence should be reformulated to express the contribution. For example, "We present an iTM that..., which exhibits superior performance..."

### Section 2

- **Line 176:** Authors should include the following references that also propose a global method based on image features:

  - Bist, C., Cozot, R., Madec, G., & Ducloux, X. (2017). Tone expansion using lighting style aesthetics. Computers & Graphics, 62, 77-86.
  - Luzardo, G., Aelterman, J., Luong, H., Philips, W., Ochoa, D., & Rousseaux, S. (2018, June). Fully-automatic inverse tone mapping preserving the content creator’s artistic intentions. In 2018 Picture Coding Symposium (PCS) (pp. 199-203). IEEE.

### Section 3

- **Figure 2:** Please change the light-blue and light-green colors on the texts in the picture, as they are very difficult to read. It would be worse if the paper was printed.

- **Line 327:** What TM operator/method is used? Is it YouTube TM as defined in Section 4?

- **Line 337:** "What’s more, we adopt" -> "Additionally, we adopt"

### Section 4

- **Table 1:** The values reported by the authors correspond to the average between all the tested images. This should be mentioned by the authors.

- **Line 564:** HDR-VDP-3 works on the color domain as rgb-bt.2020.

- **Line 637:** "What’s more" -> "In addition", "Furthermore", etc.

### Section 4.6

- The application mentioned by the author is not clear. This section should be improved.
strength_weakness: strength_weakness: - **Fine-Tuning Strategy:** The strategy to fine-tune the model on hard samples using a spatial-aware OHEM mechanism is commendable.
- **Evaluation Strategy:** The use of a perceptual encoder for objective evaluation is a strong aspect of the paper.
limitations: limitations: - **Missing References:** There are a couple of references that should be included.
paper_summary: paper_summary: This paper proposes a meta-learning framework for HDR media transmission. In this framework an HDR is compressed into a SDR with some metadata. The metadata is an image-wise MLP which can convert the SDR image to HDR. The proposed method outperforms existing ones in quality and efficiency.
strength_weakness: strength_weakness: 1.It’s a good idea to embed HDR metadata as an image-wise MLP. MLP is suitable to learn the SDR to HDR mapping. And it’s fast and has just a little overhead. 

2.The meta-learning strategy and OHEM sampling used by the proposed method can improve the generalization and adaptation capacity of this method.

3. The proposed method outperforms existing ones in quality and efficiency.
limitations: limitations: 1. In my opinion, the task of this paper is more like an HDR compression task than inverse tone mapping, the proposed method may need to be compared with some image compression methods.  

2.line 690 says that this method could reconstruct the over-exposure region accurately. But the MLP is a pixel to pixel mapping. Why it has the ability to accurately reconstruct over-exposed regions? 
  
3.The image-wise ITM distillation seems like it still requires original ITM methods to convert each input SDR image first. What is the application scenario of the distillation?
paper_summary: paper_summary: The paper proposed a meta-transfer learning framework for practical HDR/WCG media transmission by embedding image-wise metadata into their SDR counterparts for later iTM reconstruction. A meta-learning strategy and a spatial-aware online mining mechanism is adopted to train a lightweight MLP domain-wise model. This model would be transmitted alongside the SDR image, facilitating the reconstruction of the original image on HDR/WCG displays.
strength_weakness: strength_weakness: Extensive experiments demonstrate that this method gains superior performance and minimal latency with negligible overhead.
limitations: limitations: 1. It is suggested to unify the loss name in Algo.1 and Algo.2, using L1/L2 pair or MAE/MSE pair.
2. In Table 2, it is suggested to add FLOPs of each method to more intuitively show the calculation efficiency of all methods.
3. It is suggested that the authors also highlight the best experimental results by boldness in Table 2-4, just like Table 1.
4. The capitalization of articles’ titles and the format of publishers (abbreviations / full names) should be consistent in the reference section.
paper_summary: paper_summary: This paper proposes a HDR format which is upconverted from SDR using metadata as MLP. The MLP is trained via meta learning to build a base network, and fine tune according to the content via OHEM. The authors show the experimental results both subjectively and objectively.
strength_weakness: strength_weakness: With a given set of TM operators to map down HDR to SDR, using meta learning is reasonable since most existing TM operators rule based, which can be reverse model back to HDR via MLP. Fine tuning for each HDR/SDR pair based on the common trained MLP should reduce the training time.
limitations: limitations: (1) It is unclear why transmitting HDR image needs 20MB and SDR just needs 2MB and based on which metric. Though it does not affect the proposed framework, having those numbers as motivations make this considered scenario less convincing. A well tuned HDR video codec can be as efficient (same bit rate) as SDR video codec with little metadata overhead in industrial practice.
(2) Adding coordinate information into MLP is supposed to handle the local tone mapping. Because without coordinates as input, with only RGB will make only global tone mapping. Local tone mapping are often content dependent. It will be great to explain why the meta learning can learn this content dependent local tone mapping. In addition, it will be great to have experiments to demonstrate the need of using coordinates as input to MLP. 
(3) The experiments test Youtube TM.  It will be great to test other TM operators, too, to see how effective the proposed solution can be used.