Lossy Image Compression with Compressive Autoencoders
We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to address this need, but are difficult to optimize directly due to the inherent non-differentiabilty of the compression loss. We here show that minimal changes to the loss are sufficient to train deep autoencoders competitive with JPEG 2000 and outperforming recently proposed approaches based on RNNs. Our network is furthermore computationally efficient thanks to a sub-pixel architecture, which makes it suitable for high-resolution images. This is in contrast to previous work on autoencoders for compression using coarser approximations, shallower architectures, computationally expensive methods, or focusing on small images.
This work proposes a new approach for image compression using auto encoders. The results are impressive, besting the state of the art in this field. Pros: + Very clear paper. It should be possible to replicate these results should one be inclined to do so. + The results, when compared to other work in this field are very promising. I need to emphasize, and I think the authors should have emphasized this fact as well: this is very new technology and it should not be surprising it*s not better than the state of the art in image compression. It*s definitely better than other neural network approaches to compression, though. Cons: - The training procedure seems clunky. It requires multiple training stages, freezing weights, etc. - The motivation behind Figure 1 is a bit strange, as it*s not clear what it*s trying to illustrate, and may confuse readers (it talks about effects on JPEG, but the paper discusses a neural network architecture, not DCT quantization)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Lossy Image Compression with Compressive Autoencoders
We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to address this need, but are difficult to optimize directly due to the inherent non-differentiabilty of the compression loss. We here show that minimal changes to the loss are sufficient to train deep autoencoders competitive with JPEG 2000 and outperforming recently proposed approaches based on RNNs. Our network is furthermore computationally efficient thanks to a sub-pixel architecture, which makes it suitable for high-resolution images. This is in contrast to previous work on autoencoders for compression using coarser approximations, shallower architectures, computationally expensive methods, or focusing on small images.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This work proposes a new approach for image compression using auto encoders. The results are impressive, besting the state of the art in this field. Pros: + Very clear paper. It should be possible to replicate these results should one be inclined to do so. + The results, when compared to other work in this field are very promising. I need to emphasize, and I think the authors should have emphasized this fact as well: this is very new technology and it should not be surprising it*s not better than the state of the art in image compression. It*s definitely better than other neural network approaches to compression, though. Cons: - The training procedure seems clunky. It requires multiple training stages, freezing weights, etc. - The motivation behind Figure 1 is a bit strange, as it*s not clear what it*s trying to illustrate, and may confuse readers (it talks about effects on JPEG, but the paper discusses a neural network architecture, not DCT quantization)
assistant
This work proposes a new approach for image compression using auto encoders. The results are impressive, besting the state of the art in this field. Pros: + Very clear paper. It should be possible to replicate these results should one be inclined to do so. + The results, when compared to other work in this field are very promising. I need to emphasize, and I think the authors should have emphasized this fact as well: this is very new technology and it should not be surprising it*s not better than the state of the art in image compression. It*s definitely better than other neural network approaches to compression, though. Cons: - The training procedure seems clunky. It requires multiple training stages, freezing weights, etc. - The motivation behind Figure 1 is a bit strange, as it*s not clear what it*s trying to illustrate, and may confuse readers (it talks about effects on JPEG, but the paper discusses a neural network architecture, not DCT quantization)