title: GaussianImage: 1000 FPS Image Representation and Compression by 2D
    Gaussian Splatting
summary: Implicit neural representations (INRs) recently achieved great success in
  image representation and compression, offering high visual quality and fast
  rendering speeds with 10-1000 FPS, assuming sufficient GPU resources are
  available. However, this requirement often hinders their use on low-end devices
  with limited memory. In response, we propose a groundbreaking paradigm of image
  representation and compression by 2D Gaussian Splatting, named GaussianImage.
  We first introduce 2D Gaussian to represent the image, where each Gaussian has
  8 parameters including position, covariance and color. Subsequently, we unveil
  a novel rendering algorithm based on accumulated summation. Remarkably, our
  method with a minimum of 3times lower GPU memory usage and 5times faster
  fitting time not only rivals INRs (e.g., WIRE, I-NGP) in representation
  performance, but also delivers a faster rendering speed of 1500-2000 FPS
  regardless of parameter size. Furthermore, we integrate existing vector
  quantization technique to build an image codec. Experimental results
  demonstrate that our codec attains rate-distortion performance comparable to
  compression-based INRs such as COIN and COIN++, while facilitating decoding
  speeds of approximately 1000 FPS. Additionally, preliminary proof of concept
  shows that our codec surpasses COIN and COIN++ in performance when using
  partial bits-back coding.
paper_authors._id: 65f26d70addacd9d1219f16c
paper_authors.name: Xinjie Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-14T13:23:19.611Z
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paper_authors.avatarUrl: /avatars/54b809021d794f1c4b762fbc5d0c7c90.svg
paper_authors.fullname: Xinjie
paper_authors.type: user
paper_authors.user: Xinjie-Q
paper_authors._id: 65f26d70addacd9d1219f16d
paper_authors.name: Xingtong Ge
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-14T08:47:05.721Z
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paper_authors.fullname: Xingtong Ge
paper_authors.type: user
paper_authors.user: domiso
paper_authors._id: 65f26d70addacd9d1219f16e
paper_authors.name: Tongda Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-14T08:47:11.605Z
paper_authors._id: 64e805afd884b768fc610880
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paper_authors.fullname: tongda xu
paper_authors.type: user
paper_authors.user: xutongda
paper_authors._id: 65f26d70addacd9d1219f16f
paper_authors.name: Dailan He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-14T08:47:23.186Z
paper_authors._id: 640622f3d6843690270e983d
paper_authors.avatarUrl: /avatars/b76a40e460c72b90137262d597e8adbc.svg
paper_authors.fullname: Hedailan
paper_authors.type: user
paper_authors.user: Hedailan
paper_authors._id: 65f26d70addacd9d1219f170
paper_authors.name: Yan Wang
paper_authors._id: 65f26d70addacd9d1219f171
paper_authors.name: Hongwei Qin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-14T08:47:37.678Z
paper_authors._id: 6464a59a860abf0304961b60
paper_authors.avatarUrl: /avatars/07c808426eb013990906624e2ffd1941.svg
paper_authors.fullname: Qin Hongwei
paper_authors.type: user
paper_authors.user: qin2023
paper_authors._id: 65f26d70addacd9d1219f172
paper_authors.name: Guo Lu
paper_authors._id: 65f26d70addacd9d1219f173
paper_authors.name: Jing Geng
paper_authors._id: 65f26d70addacd9d1219f174
paper_authors.name: Jun Zhang
paper_title: GaussianImage: 1000 FPS Image Representation and Compression by 2D
    Gaussian Splatting
paper_summary: Implicit neural representations (INRs) recently achieved great success in
  image representation and compression, offering high visual quality and fast
  rendering speeds with 10-1000 FPS, assuming sufficient GPU resources are
  available. However, this requirement often hinders their use on low-end devices
  with limited memory. In response, we propose a groundbreaking paradigm of image
  representation and compression by 2D Gaussian Splatting, named GaussianImage.
  We first introduce 2D Gaussian to represent the image, where each Gaussian has
  8 parameters including position, covariance and color. Subsequently, we unveil
  a novel rendering algorithm based on accumulated summation. Remarkably, our
  method with a minimum of 3times lower GPU memory usage and 5times faster
  fitting time not only rivals INRs (e.g., WIRE, I-NGP) in representation
  performance, but also delivers a faster rendering speed of 1500-2000 FPS
  regardless of parameter size. Furthermore, we integrate existing vector
  quantization technique to build an image codec. Experimental results
  demonstrate that our codec attains rate-distortion performance comparable to
  compression-based INRs such as COIN and COIN++, while facilitating decoding
  speeds of approximately 1000 FPS. Additionally, preliminary proof of concept
  shows that our codec surpasses COIN and COIN++ in performance when using
  partial bits-back coding.
paper_ai_keywords: Implicit neural representations (INRs) 2D Gaussian Splatting GaussianImage Gaussian accumulated summation Gaussian-based rendering vector quantization image codec rate-distortion performance partial bits-back coding