Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields | Paper Citation Record

Observation 526dc551-4c06-4c4d-bdb4-23cf63ad4672 · outbound

This paper cites https:// docs.python.org/3/library/zlib.html.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields https:// docs.python.org/3/library/zlib.html

Reference 1

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Observation 082dd9fd-7dde-47c1-b259-5947ac9a8d4d · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Ntire 2017 challenge on single image super-resolution: Dataset and study

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.841670Z

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Observation ab1bba37-d32b-42ce-8a91-e66217433ef0 · outbound

This paper cites Variational image compression with a scale hyperprior.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Variational image compression with a scale hyperprior

Reference 3

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This paper cites Nonlinear transform coding.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Nonlinear transform coding

Reference 4

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Observation 561c0e28-5378-426d-9bfb-f1669c4d8de2 · outbound

This paper cites Better portable graphics (bpg) image for- 7 mat.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Better portable graphics (bpg) image for- 7 mat

Reference 5

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Observation bdfeb3be-3d25-497f-bca2-eea41a786c35 · outbound

This paper cites Improving image generation with better captions.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Improving image generation with better captions

Reference 6

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Observation e9a5a779-5c49-42e2-86fa-a8d10fc746a8 · outbound

This paper cites Improving image generation with better captions.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Improving image generation with better captions

Reference 7

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Observation 6f9cbc7c-fdba-4fc8-949e-cfb152a88ebb · outbound

This paper cites Overview of the versatile video coding (vvc) standard and its applications.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Overview of the versatile video coding (vvc) standard and its applications

Reference 8

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Observation 2c6ddc99-a66f-492f-b3ad-9fc87cbc0853 · outbound

This paper cites Towards image compression with per- fect realism at ultra-low bitrates.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Towards image compression with per- fect realism at ultra-low bitrates

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.781461Z

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Observation 60cbe082-d4b4-406c-841e-f698e0493c32 · outbound

This paper cites Learned image compression with discretized gaussian mixture likelihoods and attention modules.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Learned image compression with discretized gaussian mixture likelihoods and attention modules

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.771574Z

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Observation 078d47ec-b13d-4c0a-aa9e-b90cb496a5cc · outbound

This paper cites Image quality assessment: Unifying structure and texture similarity.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Image quality assessment: Unifying structure and texture similarity

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.761827Z

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Observation 5efcda54-fd7e-46fe-b661-a4d80b54e669 · outbound

This paper cites Diffusion self-guidance for control- lable image generation.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Diffusion self-guidance for control- lable image generation

Reference 12

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Observation 8679a6a3-e9c8-48dd-b1e4-de045770bd1b · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Taming transformers for high-resolution image synthesis

Reference 13

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Observation 80d5da49-d394-43ab-af9e-3bd263751e04 · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 14

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Observation 6c0241d6-08e3-4297-a2fc-5c50798536ab · outbound

This paper cites Dit4edit: Dif- fusion transformer for image editing.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Dit4edit: Dif- fusion transformer for image editing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.733642Z

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Observation 7b50ac5a-a4d9-4460-b698-e93c04040f15 · outbound

This paper cites Nvtc: Nonlinear vector transform coding.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Nvtc: Nonlinear vector transform coding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.073965Z

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Observation 656f9190-4718-491f-a57c-8d05fd7fc77b · outbound

This paper cites UniMIC: Towards Universal Multi-modality Perceptual Image Compression.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields UniMIC: Towards Universal Multi-modality Perceptual Image Compression

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:57:13.453013Z

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Observation 5807d1fc-c2b5-4ddf-ad6c-135460e404c5 · outbound

This paper cites A Residual Diffusion Model for High Perceptual Quality Codec Augmentation.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields A Residual Diffusion Model for High Perceptual Quality Codec Augmentation

Reference 18

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Observation 91a6d8a5-b00f-437f-9c2a-4be4d21876cd · outbound

This paper cites Generative adversarial nets.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Generative adversarial nets

Reference 19

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Observation 1d528529-a924-4149-a0b5-b646fc51b8e3 · outbound

This paper cites Causal contextual prediction for learned image com- pression.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Causal contextual prediction for learned image com- pression

Reference 20

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Observation fe19c473-26c4-4ee0-ac47-3489b9f6943f · outbound

This paper cites Elic: Efficient learned image compres- sion with unevenly grouped space-channel contextual adap- tive coding.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Elic: Efficient learned image compres- sion with unevenly grouped space-channel contextual adap- tive coding

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.093288Z

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Observation 36bd929a-0c3a-4ce2-b99d-809da5f86a7e · outbound

This paper cites Denoising dif- fusion probabilistic models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Denoising dif- fusion probabilistic models

Reference 22

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Observation b6394b5e-c6c4-4e6d-8152-c075a6eee951 · outbound

This paper cites High-Fidelity Image Compression with Score-based Generative Models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields High-Fidelity Image Compression with Score-based Generative Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.099633Z

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Observation a3e7223b-dd0a-4c0e-b75e-43e34722f81a · outbound

This paper cites Generative latent coding for ultra-low bitrate image com- pression.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Generative latent coding for ultra-low bitrate image com- pression

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.688077Z

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Observation 229bd899-612c-4292-ac5b-0d9a68108576 · outbound

This paper cites Imagic: Text-based real image editing with diffusion models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Imagic: Text-based real image editing with diffusion models

Reference 25

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Observation 0653a1bc-b841-4de4-9d88-065a861bb96b · outbound

This paper cites Musiq: Multi-scale image quality transformer.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Musiq: Multi-scale image quality transformer

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.110304Z

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Observation 62e074f4-5750-42f6-b0a4-c88507a6e508 · outbound

This paper cites Perco (SD): Open perceptual compression.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Perco (SD): Open perceptual compression

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.666950Z

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Observation 3b7d3bd6-01a1-4395-b7a7-7f8ed2743b3f · outbound

This paper cites an unresolved cited work.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Unresolved cited work

Reference 28

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Observation 5b0e704c-3565-4eab-b5ea-2c0bf75dcf82 · outbound

This paper cites Text + sketch: Image compression at ultra low rates.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Text + sketch: Image compression at ultra low rates

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.648245Z

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Observation 1edc32c7-1207-452b-9f17-96e769376ce0 · outbound

This paper cites MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model

Reference 30

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Observation c165a457-fadc-4ad8-9343-2400bba0ddde · outbound

This paper cites Task-driven semantic cod- ing via reinforcement learning.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Task-driven semantic cod- ing via reinforcement learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.638639Z

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Observation d203887f-80e9-4249-803b-471b6eb36c14 · outbound

This paper cites Diffusion models for image restoration and enhancement–a compre- hensive survey.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Diffusion models for image restoration and enhancement–a compre- hensive survey

Reference 32

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Observation 4f0d865e-4490-4d31-bb89-a6f503b9f562 · outbound

This paper cites Towards extreme image compression with latent feature guidance and diffusion prior.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Towards extreme image compression with latent feature guidance and diffusion prior

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.628336Z

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Observation 2855b924-e7ed-4e62-9411-ac72b6bffb41 · outbound

This paper cites RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.137063Z

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Observation 9e02b11e-f393-40e0-b6eb-9dee82727a36 · outbound

This paper cites Learned image compression with mixed transformer-cnn architectures.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Learned image compression with mixed transformer-cnn architectures

Reference 35

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Observation a7c2cda6-a888-43e8-8398-e7cf9e2c5ab0 · outbound

This paper cites Extreme im- age compression using fine-tuned vqgans.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Extreme im- age compression using fine-tuned vqgans

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.144374Z

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Observation 8b26d9df-9cae-48c2-951d-fec276646662 · outbound

This paper cites Channel-wise autoregres- sive entropy models for learned image compression.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Channel-wise autoregres- sive entropy models for learned image compression

Reference 37

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Observation 1828cfca-add8-4660-a6e4-8b0c134eb07a · outbound

This paper cites Joint autoregressive and hierarchical priors for learned image compression.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Joint autoregressive and hierarchical priors for learned image compression

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.603306Z

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Observation 28693c8d-7ea2-4173-bf8d-df3295dd8a96 · outbound

This paper cites Null-text inversion for editing real im- ages using guided diffusion models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Null-text inversion for editing real im- ages using guided diffusion models

Reference 39

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Observation 6c767d57-c991-4249-a53f-3c9640387d69 · outbound

This paper cites T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Reference 40

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Observation e782fbbd-b768-4c61-9948-8941899a5cec · outbound

This paper cites Neuralcompres- sion.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Neuralcompres- sion

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.578350Z

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Observation fd2ff971-e393-4e04-a7eb-c9c37b3fb668 · outbound

This paper cites Improving statistical fi- delity for neural image compression with implicit local like- lihood models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Improving statistical fi- delity for neural image compression with implicit local like- lihood models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.568353Z

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Observation 41d3edf7-6081-4b56-b547-d5a3e105b269 · outbound

This paper cites Addendum to gpt-4o system card: 4o image gener- ation, 2025.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Addendum to gpt-4o system card: 4o image gener- ation, 2025

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.558342Z

Source-reported events for the cited work

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Observation 7335ebd5-a92f-4182-aafb-4abff6ed6fc3 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Learning transferable visual models from natural language supervi- sion

Reference 44

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Observation 7f09300c-64d2-4ac1-b243-f779d87c6c7c · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields High-resolution image synthesis with latent diffusion models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.541827Z

Source-reported events for the cited work

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Observation 3bcb0a24-58da-484b-9fd8-52642f3ea5f6 · outbound

This paper cites Dragdiffusion: Harnessing diffusion models for interactive point-based image editing.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Dragdiffusion: Harnessing diffusion models for interactive point-based image editing

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.179087Z

Source-reported events for the cited work

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Observation 2ebdbf3a-00d1-4bac-b6b2-36f78c361c32 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Score-Based Generative Modeling through Stochastic Differential Equations

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.182472Z

Source-reported events for the cited work

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Observation 6b5a829e-a2e7-4878-95d2-b8f6c6ed1b55 · outbound

This paper cites Ex- ploring clip for assessing the look and feel of images.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Ex- ploring clip for assessing the look and feel of images

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.186083Z

Source-reported events for the cited work

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Observation 721fae6c-8ce6-4126-974a-558957e35443 · outbound

This paper cites Stylediffusion: Controllable disentangled style transfer via diffusion models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Stylediffusion: Controllable disentangled style transfer via diffusion models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.189318Z

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Observation 3936e53f-5bd1-4eba-98de-f2e058ade674 · outbound

This paper cites Learned block-based hybrid image compression.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Learned block-based hybrid image compression

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.511904Z

Source-reported events for the cited work

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Observation f8f3c140-d81e-47b9-85a9-1a89653caddd · outbound

This paper cites SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.196243Z

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Observation d7ca67a6-8f35-4e36-b3d8-61bc676f75ae · outbound

This paper cites Unifying generation and compression: Ultra-low bi- trate image coding via multi-stage transformer.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Unifying generation and compression: Ultra-low bi- trate image coding via multi-stage transformer

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.499652Z

Source-reported events for the cited work

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Observation 0a0906da-6914-4ec7-a794-9f995d614d9c · outbound

This paper cites Dlf: Extreme image compression with dual- generative latent fusion.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Dlf: Extreme image compression with dual- generative latent fusion

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.203413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 879f4328-80a6-46d3-8b05-11843d4f2f9e · outbound

This paper cites GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.206964Z

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Observation 2446c5d3-13cf-4022-b048-c893ba281870 · outbound

This paper cites Lossy image compression with conditional diffusion models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Lossy image compression with conditional diffusion models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.488923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 60a01a3a-fdfb-4f2f-bbf6-160ecf6bdfbf · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Adding conditional control to text-to-image diffusion models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.213910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb7e39a3-1c27-4aaa-8840-d28ca30233a2 · outbound

This paper cites Inversion-based style transfer with diffusion models.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Inversion-based style transfer with diffusion models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T04:57:13.217087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6b2f1d-fc1b-4767-a345-df171a2c7e54 · outbound

This paper cites Artbank: Artistic style transfer with pre-trained diffusion model and implicit style prompt bank.

Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields Artbank: Artistic style transfer with pre-trained diffusion model and implicit style prompt bank

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:13.464904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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