Generative Adversarial Networks for Extreme Learned Image Compression
We propose a framework for extreme learned image compression based on Generative Adversarial Networks (GANs), obtaining visually pleasing images at significantly lower bitrates than previous methods. This is made possible through our GAN formulation of learned compression combined with a generator/decoder which operates on the full-resolution image and is trained in combination with a multi-scale discriminator. Additionally, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, therefore only requiring the storage of the preserved region and the semantic label map. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.
 This paper proposed GAN-based framework for image compression and show improved results over several baseline methods. Although the approach is not very novel by itself, the adaption and combination of existing methods for the proposed solution is interesting. Although the bpp are consistently lower, the quality metrics used for comparison seem unclear. Pros: + The reported compression results with a GAN-based framework for large images are impressive + Comprehensive set of results with Kodak, RAISE1K and Cityscapes datasets + The paper is well written with the core results and idea being well articulated Cons: + Primary concern: The quality metrics are unclear esp. for GC models, since traditional metrics such MS-SSIM and PSNR are noted to worse and primarily visual inspection is used for comparison, making it less concrete. Would also to help include these metrics for comparison + Eqn6: .lamda balancing the distortion between GAN loss and entropy terms - can the authors elaborate on this ? Furthermore, the ensuing statement that the addition of the distortion term, results in acting like a regularizer - seems like only a conjecture, can the authors additionally comment on this as well. Minor issues: + The comparison of improvement in compression is reported using relative percentage numbers in some places as the improvement and others as lack of therein. It would help to use a common reporting notation throughout the text, this helps readability/understandability
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generative Adversarial Networks for Extreme Learned Image Compression
We propose a framework for extreme learned image compression based on Generative Adversarial Networks (GANs), obtaining visually pleasing images at significantly lower bitrates than previous methods. This is made possible through our GAN formulation of learned compression combined with a generator/decoder which operates on the full-resolution image and is trained in combination with a multi-scale discriminator. Additionally, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, therefore only requiring the storage of the preserved region and the semantic label map. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.
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 paper proposed GAN-based framework for image compression and show improved results over several baseline methods. Although the approach is not very novel by itself, the adaption and combination of existing methods for the proposed solution is interesting. Although the bpp are consistently lower, the quality metrics used for comparison seem unclear. Pros: + The reported compression results with a GAN-based framework for large images are impressive + Comprehensive set of results with Kodak, RAISE1K and Cityscapes datasets + The paper is well written with the core results and idea being well articulated Cons: + Primary concern: The quality metrics are unclear esp. for GC models, since traditional metrics such MS-SSIM and PSNR are noted to worse and primarily visual inspection is used for comparison, making it less concrete. Would also to help include these metrics for comparison + Eqn6: .lamda balancing the distortion between GAN loss and entropy terms - can the authors elaborate on this ? Furthermore, the ensuing statement that the addition of the distortion term, results in acting like a regularizer - seems like only a conjecture, can the authors additionally comment on this as well. Minor issues: + The comparison of improvement in compression is reported using relative percentage numbers in some places as the improvement and others as lack of therein. It would help to use a common reporting notation throughout the text, this helps readability/understandability
assistant
 This paper proposed GAN-based framework for image compression and show improved results over several baseline methods. Although the approach is not very novel by itself, the adaption and combination of existing methods for the proposed solution is interesting. Although the bpp are consistently lower, the quality metrics used for comparison seem unclear. Pros: + The reported compression results with a GAN-based framework for large images are impressive + Comprehensive set of results with Kodak, RAISE1K and Cityscapes datasets + The paper is well written with the core results and idea being well articulated Cons: + Primary concern: The quality metrics are unclear esp. for GC models, since traditional metrics such MS-SSIM and PSNR are noted to worse and primarily visual inspection is used for comparison, making it less concrete. Would also to help include these metrics for comparison + Eqn6: .lamda balancing the distortion between GAN loss and entropy terms - can the authors elaborate on this ? Furthermore, the ensuing statement that the addition of the distortion term, results in acting like a regularizer - seems like only a conjecture, can the authors additionally comment on this as well. Minor issues: + The comparison of improvement in compression is reported using relative percentage numbers in some places as the improvement and others as lack of therein. It would help to use a common reporting notation throughout the text, this helps readability/understandability