Title: value: On the choice of Perception Loss Function for Learned Video Compression
Authors: value: Sadaf Salehkalaibar
Truong Buu Phan
Jun Chen
Wei Yu
Ashish J Khisti
Venue: NeurIPS 2023
Keywords: value: Video Compression
Information Theory
Neural Compression

Abstract: value: We study causal, low-latency, sequential video compression when the output is subjected to both a mean squared-error (MSE) distortion loss as well as a perception loss to target realism. Motivated by prior approaches, we consider two different perception loss functions (PLFs). The first, PLF-JD,  considers the joint distribution (JD) of all the video frames up to the current one, while the second metric, PLF-FMD,  considers the framewise marginal distributions (FMD) between the source and reconstruction. Using information theoretic analysis and deep-learning based experiments, we demonstrate that the choice of PLF can have a significant effect on the reconstruction, especially at low-bit rates. In particular, while the reconstruction based on PLF-JD can better preserve the temporal correlation across frames, it also imposes a significant penalty in distortion  compared to PLF-FMD and further makes it more difficult to recover from errors  made in the earlier output frames. Although the choice of PLF decisively affects  reconstruction quality, we also demonstrate that it may not be essential to commit to a particular PLF during encoding and the choice of PLF can be delegated to the decoder. In particular, encoded representations generated by training a system to minimize the MSE (without requiring either PLF) can be  {\em near universal}  and can generate close to optimal reconstructions for either choice of PLF at the decoder.  We validate our results using (one-shot) information-theoretic analysis, detailed study of the rate-distortion-perception tradeoff of the Gauss-Markov source model as well as deep-learning based experiments on moving MNIST and KTH datasets.


Review discussion:
This paper conduct experiments and give some theoretical analysis of the perception loss functions for learned video compression. It is valuable when designing and training a video compression pipeline.
1.  The datasets used in the paper are too simple. It is questionable that the conclusions still work for the datasets that are closer to the real world. It is suggested to provide more experiments on the widely used datasets in learned video compression methods, such as UVG, MCL-JCV, and HEVC Class B datasets. Most papers conduct their experiments on these datasets.
So we can see more performance comparisons and verify the proposed method is not a toy example.
2.  Considering this paper focus on perception loss, why not try some metrics about perceptual quality?
For example, the experiments can also provide widely used perceptual metrics such as  LPIPS.
In the experiments, I do not know how the proposed method precisely controls the bit rate to make the rates of different methods to be identical.
See the weakness part.
No ethics review needed.
We thank Reviewer 1Qbf for considering our work “valuable when designing and training a video compression pipeline”. Please find your concerns addressed below.
1) The datasets used in the paper are too simple. It is questionable that the conclusions still work for the datasets that are closer to the real world. It is suggested to provide more experiments on the widely used datasets in learned video compression methods, such as UVG, MCL-JCV, and HEVC Class B datasets. Most papers conduct their experiments on these datasets.
So we can see more performance comparisons and verify the proposed method is not a toy example.
Reply: Please see the global rebuttal, the part on “scope of experimental results”.
2) Considering this paper focuses on perception loss, why not try some metrics about perceptual quality?
For example, the experiments can also provide widely used perceptual metrics such as LPIPS.
Reply:  Our focus is on the RDP tradeoff and validating these results using DL experiments. We chose the perception loss functions as they are natural counterparts of the theoretical metrics used. We also provide quality assessment metrics for your consideration. In the KTH experiment, we computed LPIPS, which is known as a full-reference perceptual metric for images. We compare LPIPS for each reconstructions (MMSE, 0-PLF-FMD and 0-PLF-JD) for each timeframe.
LPIPS (lower is better) on KTH dataset:
1st frame: MMSE - 0.1036, 0-PLF-FMD - 0.0584, 0-PLF-JD - 0.0584
2nd frame: MMSE - 0.0521, 0-PLF-FMD - 0.0313, 0-PLF-JD - 0.0594
3rd frame: MMSE - 0.0413, 0-PLF-FMD - 0.0232, 0-PLF-JD - 0.0613
This result aligns with our results on distortion loss, as the MMSE and 0-PLF-FMD tend to correct the reconstruction (so the ground truth and reconstruction look more similar over time).
On the other hand, due to error permanence, the 0-PLF-JD reconstructions become different from their source sequence, causing the score to go up.
Finally, we would like to thank you for this suggestion. We are going to add this set of experiments in the new version of our work.
3) In the experiments, I do not know how the proposed method precisely controls the bit rate to make the rates of different methods to be identical.
Reply: We control the bit rate by controlling the dimension of the latent variables D (encoder’s output) and the number of quantization levels L. The bit rate can then be calculated by $D \times log_2(L)$.
While this approach is a bit sub-optimal, it makes controlling the bitrate much easier compared to the well-known approach by Balle et. al (see ref [13] of our paper).
Also, it has been adopted in previous prior works for learned image compression, such as (ref [11], [16] and [12] of our paper).
We hope this answers your concern. We also ask you to kindly reconsider your evaluation.
- Relatively readable paper
- Valuable insights into the generative video compression field
- The theoretical results are validated in experiments on small datasets (Moving MNIST and KTH).
- Intuitively, why would we only consider frame level marginal distributions? Just like pixel-level marginal distributions don’t make sense, this should lead to massive temporal artefacts/flickering in the low rate regime.
While e.g. [7] used only a frame-level discriminator, they use warping for temporal consistency.
- I found the proof of Theorem 1 to be quite similar to the proof of Thm. 2 in Appendix B in (Blau & Michaeli, 2019). It seems to me that proof also works as-is in the setting where you condition on previously encoded frames, as there are no assumptions on how the MSE optimized reconstruction is obtained (so whether it is using information from previous frames or not doesn’t seem to matter in the derivation). I would appreciate if the author could clarify differences here.
- Experiments are limited to toy data.
Minor (not affecting rating): *The supplement is very long* (30+ pages) and the main paper contains 24 references to it. It seems to me the authors have a lot more to say than fits into 9 pages. I'm not sure what to do with this but it feels slightly weird. I did not read everything in the supplement.
Toy data / causal setting.
No ethics review needed.
We thank Reviewer tXFk for considering our work a “relatively readable paper” and “valuable insights into the generative video compression field”. Please find your concerns addressed below.
1) intuition on the use of PLF-FMD in the low-rate regime
Reply: Please note that in all our experiments the neural architecture we implement is similar to reference [7]. Our architecture also includes warping to match the locations of the current source frame using the decoded flow fields for temporal consistency. We demonstrate that such an architecture in conjunction with a frame-level discriminator (PLF-FMD metric in our paper) appears to provide the most desirable reconstruction when the I-frame is compressed at low bit-rates. This is because the proposed loss function is flexible enough to introduce meaningful corrections in the reconstruction in later frames as more information is received.
While this might introduce some flickering effect, the effects are not random since the (high-fidelity) reconstructions tend to sync up with the source, as shown in Figures 2 and 7.
In contrast, a discriminator that preserves joint distribution across frames (PLF-JD metric in our paper) is not only considerably more challenging to implement (due to the need for a different conditional decoder at each step/timeframe), but also suffers from an “error permanence” phenomenon — errors made in earlier frames do not get corrected in later frames, even when the decoder has sufficient information for doing so. We suspect that similar considerations between the joint and framewise metrics were made in prior works such as references [6] and [7], that also implement a frame-level discriminator. In [7], the authors mention (Section 3. 5, second paragraph) that their attempt to include multiple frames in their GAN (a version of our JD) “did not significantly alter reconstructions”.
Our work is the first attempt to theoretically study the impact of PLF-FMD and PLF-JD metrics using the framework of rate-distortion-perception (RDP) tradeoff. Our results in Theorems 1, 2, 5, as well as Table 2 (in the Appendix) provide insightful comparisons between the two metrics, which we further validate using experimental results. We note that our work is theoretically grounded in the study of RDP tradeoffs, and as is the case of prior works of Blau and Michaeli [11] and Zhang et al [16],  we have also presented our experimental studies on two simpler datasets. We expect that larger scale experimental studies that incorporate more complex datasets will result in similar conclusions.
For example, we expect errors made in the previous frames to continue to propagate when PLF-JD metric is used, regardless of which dataset is used and PLF-FMD to be more flexible in correcting such errors. We hope that you agree with the importance of the PLF-FMD metric and better appreciate its importance in the context of prior work.
Finally, we note that there are other rate regimes where the choice of PLF-JD is preferred over PLF-FMD (see Table 2 of Appendix F and experimental results in Appendix J.5).
2) On the comparison of Theorem 1 with Thm. 2 in Appendix B in (Blau & Michaeli, 2019)
Reply: While direct extension of the achievable scheme in Thm 2 of (Blau & Michaeli, 2019) [11] by conditioning on previously encoded frames suffices to achieve the “factor of 2” bound in our Theorem 1, we note that (in contrast to [11]) the main result in Theorem 1 involves an exact characterization (see Eq. 5) of the distortion region. The “factor of 2 bound”  is derived  (see Eq. 6)  as a consequence of this characterization. To establish our main result, in addition to the “achievability part”, we had to also establish a converse which is not present in [11].
Nevertheless we have not claimed significant novelty in this part of the work.  As noted in the paragraph immediately below Theorem 1, our result in Eq. (5) is a generalization of an analogous result for single frame setting in Zhang et al (see reference [16] in our paper).  The intuitive reason why Theorem 1 follows as a natural extension of previous works (on single frame)  is because it considers a fixed encoder setting without explicitly considering the rate constraints.
Nevertheless this setting provides insightful comparisons between the PLF-FMD and PLF-JD metrics as discussed in Section 3. We note that when the entire rate-distortion-perception tradeoff is considered our theoretical analysis is considerably more challenging than the prior works on single-frame setting in references [11] and [16]. Please look at the global rebuttal, the part on “Novelty of Theoretical Analysis” for a brief review of our theoretical challenges in the work.
3) Experiments are limited to toy data.
Reply: Please see the global rebuttal, the part on “scope of experimental results”.
4) Causal setting
Reply: For video compression, there are two frameworks being considered in the current literature: causal and non-causal frameworks. For the non-causal framework, people often compress the whole video (or a block of multiple subsequent frames), as in the following reference
Habibian, Amirhossein, et al. "Video compression with rate-distortion autoencoders." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.
We would like to emphasize that results from previous RDP tradeoffs for images should naturally hold for this setting (with PLF-JD metric), specifically the factor-of-two bound and universality property since we can consider the whole video as a whole 3D image in this sense. This setting, however, is not efficient in real-time applications due to the delay in block compression/decompression. Most recent neural video compression methods, on the other hand, often consider the causal setting.
We hope to have addressed all your concerns satisfactorily and kindly ask you to consider increasing the score.
Thanks for the reply. I will stick to my rating, and hope future work will apply this to larger datasets.
We would like to inform you that we have already obtained the results of
the experiments on the UVG dataset. According to them, the error
permanence phenomenon shows up in the JD reconstructions.
That is, it
propagates the flaws in the color tone of the previous low-rate
reconstruction in the future ones.
However, FMD reconstructions do not
suffer from such a problem. The distortions over 3900 UVG samples are given as
follows:
|       | MMSE | PLF-FMD     | PLF-JD
| Distortion (MSE)      | 0.0026       | 0.0032   | 0.0168
This confirms the error permanence phenomenon for PLF-JD.
Currently, we are not able to send you the visualization of our results
since according to the conference timeline, the deadline for uploading a
pdf file has passed.
But, we are going to include these results in the new
version of our work
This paper demonstrates the proposed assertions through theoretical derivations and detailed experiments, which are convincing enough.
In addition, these conclusions provide a good guideline basis for subjective optimization of learned video compression methods.
Since this paper is oriented to the discussion of learned video compression, the experimental setup may be too simple to have generalized conclusions.
For example, the used sequences (e.g., MNIST and KTH) for evaluation are too homogeneous to present the characteristics of videos. It should be better to have some comparisons on more complex sequences such as the UVG dataset. It is critical to illustrate whether these findings would be useful in practical applications.
As mentioned above, it is suggested to have some experiments on sequences with more complex motion and coded bits to examine the proposed findings in addition to those toy examples in the submission.
No.
No ethics review needed.
We thank reviewer QNA6 for considering our work as “a good guideline basis for subjective optimization of learned video compression”. Please find your concern addressed below
1) Since this paper is oriented to the discussion of learned video compression, the experimental setup may be too simple to have generalized conclusions.
For example, the used sequences (e.g., MNIST and KTH) for evaluation are too homogeneous to present the characteristics of videos. It should be better to have some comparisons on more complex sequences such as the UVG dataset. It is critical to illustrate whether these findings would be useful in practical applications.
Reply: The current empirical setup for comparing neural codecs often entails training the networks on the Youtube/Kinetics/Vimeo90k dataset (which can encompass nearly a million frames) and subsequently testing on high-resolution videos like UVG and MCL-JCV.
While we are diligently working towards generating results on UVG/MCL-JCV, it may not be feasible to achieve this within the constraints of the rebuttal process. The scale of these datasets and the complex nature of GAN training make it challenging to complete within a limited timeframe.
We would like to emphasize that our work's major contribution lies in being the first to explore the impact of perception loss functions on the RDP tradeoff for video compression, with a strong theoretical focus and we use our experimental results to confirm our theoretical findings. Prior works on the rate-distortion-perception tradeoff, such as Blau and Michaeli (ICML 2019) [11] and Zhang et al. (NeurIPS 2021) [16], also presented empirical results on simpler image datasets (MNIST and SVHN) to demonstrate their theoretical findings.
While our experiments on two datasets MovingMNIST and KTH are homogeneous (lacking other effects such as zooming, camera moving, etc.). As it is illustrated in Fig. 1(b), Fig. 2(a) and Fig. 7 (in the Appendix), the error permanence phenomenon, associated with joint distribution-based discriminators in the low-rate regime, occurs in even these two homogeneous datasets. This observation strongly suggests that the same effect is likely to be more pronounced in complex non-homogeneous videos.
For other results, Table 1 validates the existence of a "factor of 2" distortion bound through the use of a frame-level discriminator, aligning seamlessly with Theorem 1.
Additionally, Fig. 3 serves as a compelling testament to the universality of MMSE-based representations. Building upon their theoretical foundation, we believe these outcomes to extend their applicability to diverse datasets.
Finally, we note that these findings also come with applications. The universality aspect of MMSE representation emerges as crucial during the training of perceptual video compression models. Given that perceptual reconstruction frequently generates novel details not present in the source frame (especially when using the FMD-JD loss), compressing motion flow vectors between the current frame and the prior perceptual reconstruction necessitates a higher bit allocation compared to utilizing the MMSE representation.
Therefore, this suggests that the recommended approach is to train end-to-end compression exclusively using the rate-distortion loss.
Subsequently, users have the flexibility to opt for their preferred decoder based on their perceptual preferences.
We hope this response alleviates your concerns about the need to implement our experiments on more complex datasets. We also kindly request you to consider increasing the score.
Thank you for your response. I will keep my rating.
We would like to inform you that we have already obtained the results of
the experiments on the UVG dataset. According to them, the error
permanence phenomenon shows up in the JD reconstructions.
That is, it
propagates the flaws in the color tone of the previous low-rate
reconstruction in the future ones.
However, FMD reconstructions do not
suffer from such a problem. The distortions over 3900 UVG samples are given as
follows:
|       | MMSE | PLF-FMD     | PLF-JD
| Distortion (MSE)      | 0.0026       | 0.0032   | 0.0168
This confirms the error permanence phenomenon for PLF-JD.
Currently, we are not able to send you the visualization of our results
since according to the conference timeline, the deadline for uploading a
pdf file has passed.
But, we are going to include these results in the new
version of our work
Originality and quality of this paper are pretty good. The PLF-JD and PLF-FDM are two different but both reasonable perceptual metrics used for optimization of video compression models. This is the first paper that studies the RDP issue in video compression and the results and conclusions are insightful. Some important conclusions regarding RDP include:
(1) There is a significant penalty in distortion when using PLF-JD in the low-rate regime.
(2) While PLF-JD preserves better temporal consistency across video frames, it suffers from the permanence of error phenomenon in which the mistakes in reconstructions propagate to future frames.
The universal encoded representations for video compression are studied as well, which are demonstrates to be always held when using PLF-FDM (similar to compressing images sequentially).
Besides, the effectiveness of RDP in video compression models is well verified by theoretical analysis in Gaussian-Markov sources.
(1)	(Perhaps not as a weakness) I am wondering whether these conclusions of RDP in video compression can be extended to distortion-perception tradeoff in other video generation / restoration tasks. The joint-frame loss PLF-JD and the per-frame loss PLF-FDM should hold similar conclusions for video generation.
(2)	The state-of-the-art neural video compression framework contains a motion encoder/decoder and a residual encoder/decoder, which are effective for high-resolution videos. It seems this paper studies the other neural video compression framework that does not explicitly transmit motion information. How to apply the proposed theory to such motion-residual-separated video compression framework?
See the abovementioned “weaknesses”.
Limitations are discussed in the final section of Appendix. This paper is in the field of video compression which does not involve strong negative societal impact.
No ethics review needed.
We thank Reviewer pcBB for considering our work “the first paper that studies the RDP issue in video compression” and praising its “originality and quality”. Please find your concerns addressed below.
1) (Perhaps not as a weakness) I am wondering whether these conclusions of RDP in video compression can be extended to the distortion-perception tradeoffs in other video generation/restoration tasks. The joint-frame loss PLF-JD and the per-frame loss PLF-FDM should hold similar conclusions for video generation.
Reply: Thank you for pointing this out.
Indeed, the tasks of restoration can be interpreted as a video compression problem. For the causal video restoration task, the incoming corrupted frame can be considered as a message $M_i$ that the decoder needs to decode (restore) and the encoder simply sends $M_i$ by corrupting the “clean” source image $X_i$.
As such the factor-of-two bound should hold for the  PLF-FMD metric (following [1], which considers the image restoration setting), but not for the PLF-JD metric, by following the same arguments presented in our paper.
In the context of video generation, recent SOTA methods (StyleGAN-V, MoCoGAN) frequently adopt implicit generation approaches, such as GANs, that bypass distortion loss. In this context, PLF-JD appears as a generally preferable choice, given its emphasis on temporal consistency. Within the VAE framework, methods like He et al.'s "Probabilistic Video Generation using Holistic Attribute Control" (ECCV 2018), often incorporate a distortion term. This category of models frequently produces blurry videos.
Consequently, we posit that incorporating a PLF-FMD term could enhance the generation of realistic frames while sacrificing temporal consistency.
Conversely, utilizing PLF-JD might introduce training challenges (as elaborated in the "Significance of Results" section in the rebuttals). We believe this is an interesting question for future research.
For non-causal video generation/restoration, where a video is treated as a 3D image, PLF-JD should be a better metric overall since previous results for RDP image compression (factor of 2 bound, universality) should hold with the PLF-JD metric. This is because the proof from previous works does not make assumptions on the dimensionality of the input.
As such, the implication for the non-causal case is more straightforward (similar to results for images).
In fact, this is also the message that we want to send to the community, that theoretical results for rate-distortion-perception depend heavily on the coding scheme one is using.
2) The state-of-the-art neural video compression framework contains a motion encoder/decoder and a residual encoder/decoder, which are effective for high-resolution videos. It seems this paper studies the other neural video compression framework that does not explicitly transmit motion information. How to apply the proposed theory to such motion-residual-separated video compression framework?
Reply: On the theoretical side, the current framework can be adapted to incorporate the motion-residual-separated architecture. At each step, the source includes both motion and residual contents. The message that is sent to the decoder can contain two parts where one of them compresses the motion content while the other one is the compressed version of the residual content. The operational RDP region is defined similarly to Definition 2.
So, all the previous discussions hold with a simple redefinition of the random variables.
On the experimental side, many of the current state-of-the-art architectures do indeed contain a motion encoder/decoder and residual encoder/decoder. This is also replicated in our experiment section, where the scale-space-flow module is responsible for computing the optical flow field and warping (see ref [32] of our paper), and the conditional module for efficient residual compression (see ref [4] of our paper). Our proposed theory is general and applicable to this architectural design, which has been illustrated in the experiment section.
Thanks for your responses. They address my previous concerns well.  I will keep my rating as accept.
We thank the reviewers for acknowledging the significance of the work. Reviewer pcBB notes that our work is the first paper that studies the RDP issue in video compression and finds the results and conclusions to be insightful.
Similarly reviewers QNA6, txFk and 1Qbf have noted the significance of our results and conclusions to learned video compression. Reviewers QNA6 and txFk have also appreciated the quality of the presentation. In our rebuttal, we have taken into account the feedback provided by the reviewers and included some additional experimental results involving LPIPS score as suggested by the reviewers (see pdf file below). We note that the conclusions are largely consistent with the original results in the paper. We provide some general comments that address the concerns raised by the reviewers.
**Scope of experimental results:** Some of the prior experimentally oriented papers in neural video compression involve extensive training (Youtube/Kinetics) and complex testing (UVG) to validate the use of new metrics or neural architectures. Our work's major contribution lies in being the first to explore the impact of perception loss functions (PLFs) on the rate-distortion-perception (RDP) tradeoff, with a strong theoretical focus. The purpose of experiments is to provide qualitative visualization of our theoretical results using simpler datasets.
For example in Fig. 1(b), Fig. 2(a) and Fig. 7 (in the Appendix) we demonstrate the error permanence phenomenon in the low-rate regime associated with a discriminator network that considers the joint distribution across video frames. Since this observation is inherent to the nature of the PLF as verified by our theoretical result (Thm 5), we do not expect this property to be specific to our choice of datasets and can be generalized to other datasets.
Similarly Table 1 demonstrates the “factor of 2” bound in distortion when a frame-level discriminator is used during training consistent with Theorem 1 in the paper.
Likewise Fig. 3 demonstrates the sufficiency of MMSE representations for achieving near optimal performance. Since all these results are grounded in our theoretical analysis we again expect other datasets to yield similar conclusions. Note that prior works that provided theoretical study of the RDP tradeoff, such as the works of Blau and Michaeli (ICML 2019) [11] and Zhang et al (NeurIPS 2021) [16] also demonstrated their empirical results on simpler datasets such as MNIST and SVHN. Based on these considerations we request the reviewers who have provided us lower scores simply because the experimental results did not include additional datasets reconsider their evaluation. We also note that while we are making every effort to produce results on additional datasets, it may not be practically feasible for us to finish these results during the rebuttal process as these datasets are extremely large and the GAN training may not complete in such a short timeframe.
Finally, we added experimental results with LPIPS score (requested by reviewer 1Qbf) and they are consistent with our previous observations (see the pdf file).
**Significance of Results:** We establish that encoded representations designed to minimize the MSE distortion loss can suffice to achieve near optimal distortion-perception tradeoff for both the joint and per-frame perception loss constraints. Our work is the first one to introduce such a principle of universality in the context of learned video compression previously studied in the context of image compression [16, 25].
In fact we believe that universality of MSE representations is far more significant in the context of learned video compression. Given that perceptual reconstruction frequently generates novel details not present in the source frame, compressing motion flow vectors between the current frame and the prior perceptual reconstruction necessitates a higher bit allocation compared to utilizing the MMSE representation.
Hence, a recommended approach is to train end-to-end compression exclusively to minimize MSE and use the method in the proposed work to achieve a (near-optimal) tradeoff between the distortion and perception losses as desired by the user. We also provide insightful analysis using the framework of RDP to compare two commonly used PLFs. We demonstrate (using theoretical analysis and experimental results) that the PLF using a joint distribution constraint can be overly restrictive at low compression rates and can lead to an undesired error permanence phenomenon.
In contrast, PLFs based on per frame metric can have enough flexibility to perform desirable corrections in the reconstruction frames as more information is available to the decoder and may be preferred at low bit rates.
However, in some other rate regimes, the choice of PLF-JD is preferred over PLF-FMD (see Table 2 of Appendix F and experimental results of Appendix J.5).
**Novelty of Theoretical Analysis:** The study of RDP region for learned video compression is considerably more challenging than the study of RDP function for a single frame in prior works. As established in Thm 3, the RDP region (for first-order Markov sources) involves a tradeoff between the compression rate assigned to each frame. Even for Gaussian sources, the RDP region does not have a simple closed form which makes the proof of optimality of Gaussian reconstructions (Thm 4) more involved.
As a result a significant effort was devoted towards obtaining insights in various operating regimes. We summarized the main results in Table 2 in Appendix F and in Thm 5.
Furthermore the proof of universality in Thm 6 (for Gaussian sources) is considerably more challenging as one has to consider the achievability of the entire RDP region as opposed to just points on the boundary of RDP function in [16].
Finally the results on the fixed-encoder setup are more general than prior works that required a characterization of the information RDP region, which we do not require.

Meta-review:
role: PC
data: comment: value: The paper received 1xAccept, 1xWeak Accept, 1xBorderline Accept and 1xBorderline Reject from four Reviewers.

The authors provided responses.

After carefully checking the paper, the reviews, and the authors' responses the ACs agree with the reviewers that the work has merits, is of interest for the community and it can be published.

The authors are asked to include (part of) the information/details provided in their responses to the reviewers' comments and further refine their paper for camera ready.

Decision: Accept (poster)