Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper proposes an animation video super-resolution method. 
First, the authors build the proposed AVC dataset by collecting low-quality videos and by generating pseudo-HR videos. Pseudo-HR videos are obtained by rescaling low-quality videos and super-resolving them with a conventional SR method. 
Second, the degradation of animations are modeled by the combination of conventional corruption artifacts and learnable convolutional layers. The authors train their degradation model with the AVC dataset LR & pseudo-HR pairs.
Third, the proposed model architecture, window-based RNN is trained by the pseudo-HR dataset and the generated LR videos.

strengths_and_weaknesses: [Strengths]

This paper tackles the animation super-resolution problem by observing the distribution of animation and natural videos differ. The observation is validated by comparing the SoTA methods and the proposed method on animations.
In order to handle the unknown degradation artifacts that are hard to model, the proposed method combines the known operations (blur, noise, FFMPEG) and learnable operations (conv layers). This is a novel and effective approach.

Also, the authors construct pseudo-HR data from their observations which exhibits validity through experimental results.

[Weaknesses]

However, I wonder what the main principle is in the collected & generated dataset.
The video selection seems to be manual (which is fine) and the rescaling factor selection seems to rely on human perception. Were there any analyses that are not shown in the paper?
The authors use NIQE and MANIQA metrics. Any correlations with those metrics?
If the selection process is manual, it doesn’t seem to be a reproducible and scientific approach.

questions: L172-174 and Fig. 5. 
I don’t exactly understand what the process of input-rescaling strategy is. Is input low-quality video frame downsampled and then super-resolved from a BasicOP-Only method?

Figure 9

Is this a result of equation (2)-like cascade of blur, noise, and LBO?
In equation (2), does two LBO share weights?

L318 

Blur and noise are explicitly modeled in equation (2) even without LBO. Why does LBO learn to blur and noise images?

L306

Is one LBO learned per a video?
limitations: Limitations discussed in the manuscript.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper aims to improve blind video super-resolution (VSR) for animation videos. It combines two categories of degradation synthesis processes (classic basic operator and neural networks) to overcome both limitations. Besides, the authors build a new high-quality animation video dataset for animation VSR. Moreover, the paper designs an efficient network structure for VSR based on sliding window structures and multi-scale design. The experiments reveal that the proposed model outperforms other state-of-the-art models and validate the effectiveness of the model design.
strengths_and_weaknesses: Strengths:
1. It is interesting and seems promising to combine classic basic operators and neural networks together and learn basic operators by neural networks.
2. The authors build a large-scale high-quality animation video dataset for animation VSR.
3. There are plenty of experiments in the paper to validate the effectiveness of the model design. The experiments demonstrate that the proposed methods perform better than other state-of-the-art models and the combination of classic basic operators and neural networks is beneficial.

Weaknesses:
1. The use of the “input-rescaling strategy” is not demonstrated well. How do the users manually select the pseudo HR? It seems time-consuming to do it.
2. The intuition behind sliding window structure and multi-scale design is not demonstrated well. How to distinguish it from FRVSR? FRVSR is also based on sliding windows.

questions: 1. The author should present how to use the “input-rescaling strategy” in detail and discuss the time-consuming issue.
2. The author should differentiate the proposed method from FRVSR, especially the sliding window design.
limitations: The authors adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper aims to develop an effective and efficient real-world video super-resolution (VSR) method to restore real-world low-quality animation videos. The authors first proposed to learn basic operators with tiny neural networks (with 2 or 3 conv layers) to incorporate the learned networks into the degradation synthesis process. Secondly, the authors propose a new dataset AVC which is a large-scale high-quality animation video dataset for training and evaluation. The authors perform experiments on AVC dataset and demonstrate superior performance compared to state-of-the-art models.
strengths_and_weaknesses: Strength: Instead of using one large neural network for the whole degradation process or classic basic operators without any learning capability, this paper proposes a novel method to learn basic operators with tiny neural networks. Meaningful discussion is also a strength of this paper (e.g. discussion on input-rescaling strategy, visualization of learned LBOs). Further, this paper presents a new dataset that contains more than a total of 50,000 frames, which is one of the main contributions of this paper.

Weakness: The writing can be improved. Section 4.2 is particularly confusing. The novelty of the approach seems limited. In particular, many components presented in this work are simple modifications from existing work (e.g. combining the classic basic operators and existing neural-network-based operators). Further, the proposed supervised manner of training heavily relies on an input-rescaling strategy, which restricts the scalability of work to the animation videos.

questions: 1. Does the proposed input-rescaling strategy only work for animation videos? Since the authors anticipate that the input-rescaling strategy will not work well for natural videos due to textures, ablation studies on a texture could be helpful to quantitively investigate the scalability of this work.

2. How to determine the best rescaling factor of inputs for each video? Since the optimal rescaling factor varies on each video, pseudocode to determine the rescaling factor can help understand the overall process.

limitations: Societal Impact: There is no significant negative societal impact of this research.
review_title: null

Meta-review: metareview: The paper proposes a method for super-resolution of animation videos. The contribution is three-fold: a new approach to learned image degradations, a dataset of high-resolution animation videos, and a multiscale model architecture. The method demonstrated good empirical results while being substantially faster than prior approaches.

All reviewers are positive about the paper (although to a different degree) and mention that the proposed "learned basic operators" are interesting and new, the dataset is valuable, and the method is thoroughly evaluated and works well. 

Overall, the paper is a solid application paper with some interesting new ideas and I recommend acceptance.  I highly encourage the authors to update the paper based on the discussions with the reviewers, in particular with the details on dataset creation and the rescaling factor.
confidence: Certain
Decision: Accept