SIEDD: Shared-Implicit Encoder with Discrete Decoders

• URL: http://arxiv.org/abs/2506.23382v1
• Date: Sun, 29 Jun 2025 19:39:43 GMT
• Title: SIEDD: Shared-Implicit Encoder with Discrete Decoders
• Authors: Vikram Rangarajan, Shishira Maiya, Max Ehrlich, Abhinav Shrivastava,
• Abstract summary: Implicit Neural Representations (INRs) offer exceptional fidelity for video compression by learning per-video optimized functions.<n>Existing attempts to accelerate INR encoding often sacrifice reconstruction quality or crucial coordinate-level control.<n>We introduce SIEDD, a novel architecture that fundamentally accelerates INR encoding without these compromises.
• Score: 36.705337163276255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Implicit Neural Representations (INRs) offer exceptional fidelity for video compression by learning per-video optimized functions, but their adoption is crippled by impractically slow encoding times. Existing attempts to accelerate INR encoding often sacrifice reconstruction quality or crucial coordinate-level control essential for adaptive streaming and transcoding. We introduce SIEDD (Shared-Implicit Encoder with Discrete Decoders), a novel architecture that fundamentally accelerates INR encoding without these compromises. SIEDD first rapidly trains a shared, coordinate-based encoder on sparse anchor frames to efficiently capture global, low-frequency video features. This encoder is then frozen, enabling massively parallel training of lightweight, discrete decoders for individual frame groups, further expedited by aggressive coordinate-space sampling. This synergistic design delivers a remarkable 20-30X encoding speed-up over state-of-the-art INR codecs on HD and 4K benchmarks, while maintaining competitive reconstruction quality and compression ratios. Critically, SIEDD retains full coordinate-based control, enabling continuous resolution decoding and eliminating costly transcoding. Our approach significantly advances the practicality of high-fidelity neural video compression, demonstrating a scalable and efficient path towards real-world deployment. Our codebase is available at https://github.com/VikramRangarajan/SIEDD .

Related papers

• StableCodec: Taming One-Step Diffusion for Extreme Image Compression [19.69733852050049]
  Diffusion-based image compression has shown remarkable potential for achieving ultra-low coding (less than 0.05 bits per pixel) with high realism.<n>Current approaches require a large number of denoising steps at the decoder to generate realistic results under extreme constraints.<n>We introduce StableCodec, which enables one-step diffusion for high-fidelity and high-realism extreme image compression.
  arXiv  Detail & Related papers  (2025-06-27T07:39:21Z)
• Plug-and-Play Versatile Compressed Video Enhancement [57.62582951699999]
  Video compression effectively reduces the size of files, making it possible for real-time cloud computing.<n>However, it comes at the cost of visual quality, challenges the robustness of downstream vision models.<n>We present a versatile-aware enhancement framework that adaptively enhance videos under different compression settings.
  arXiv  Detail & Related papers  (2025-04-21T18:39:31Z)
• REGEN: Learning Compact Video Embedding with (Re-)Generative Decoder [52.698595889988766]
  We present a novel perspective on learning video embedders for generative modeling.<n>Rather than requiring an exact reproduction of an input video, an effective embedder should focus on visually plausible reconstructions.<n>We propose replacing the conventional encoder-decoder video embedder with an encoder-generator framework.
  arXiv  Detail & Related papers  (2025-03-11T17:51:07Z)
• High-Efficiency Neural Video Compression via Hierarchical Predictive Learning [27.41398149573729]
  Enhanced Deep Hierarchical Video Compression-DHVC 2.0- introduces superior compression performance and impressive complexity efficiency. Uses hierarchical predictive coding to transform each video frame into multiscale representations. Supports transmission-friendly progressive decoding, making it particularly advantageous for networked video applications in the presence of packet loss.
  arXiv  Detail & Related papers  (2024-10-03T15:40:58Z)
• Standard compliant video coding using low complexity, switchable neural wrappers [8.149130379436759]
  We propose a new framework featuring standard compatibility, high performance, and low decoding complexity. We employ a set of jointly optimized neural pre- and post-processors, wrapping a standard video, to encode videos at different resolutions. We design a low complexity neural post-processor architecture that can handle different upsampling ratios.
  arXiv  Detail & Related papers  (2024-07-10T06:36:45Z)
• Compression-Realized Deep Structural Network for Video Quality Enhancement [78.13020206633524]
  This paper focuses on the task of quality enhancement for compressed videos. Most of the existing methods lack a structured design to optimally leverage the priors within compression codecs. A new paradigm is urgently needed for a more conscious'' process of quality enhancement.
  arXiv  Detail & Related papers  (2024-05-10T09:18:17Z)
• Low-complexity Deep Video Compression with A Distributed Coding Architecture [4.5885672744218]
  Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce temporal redundancy. Traditional distributed coding methods suffer from a substantial performance gap to predictive coding ones. We propose the first end-to-end distributed deep video compression framework to improve rate-distortion performance.
  arXiv  Detail & Related papers  (2023-03-21T05:34:04Z)
• GAN-Based Multi-View Video Coding with Spatio-Temporal EPI Reconstruction [19.919826392704472]
  We propose a novel multi-view video coding method that leverages the image generation capabilities of Generative Adrial Network (GAN) At the encoder, we construct atemporal Epipolar Plane Image (EPI) decoder and further utilize a convolutional network to extract the latent code of a GAN as Side Information (SI) At the side, we combine SI and adjacent viewpoints to reconstruct intermediate views using the GAN generator.
  arXiv  Detail & Related papers  (2022-05-07T08:52:54Z)
• A Coding Framework and Benchmark towards Low-Bitrate Video Understanding [63.05385140193666]
  We propose a traditional-neural mixed coding framework that takes advantage of both traditional codecs and neural networks (NNs) The framework is optimized by ensuring that a transportation-efficient semantic representation of the video is preserved. We build a low-bitrate video understanding benchmark with three downstream tasks on eight datasets, demonstrating the notable superiority of our approach.
  arXiv  Detail & Related papers  (2022-02-06T16:29:15Z)
• Adversarial Neural Networks for Error Correcting Codes [76.70040964453638]
  We introduce a general framework to boost the performance and applicability of machine learning (ML) models. We propose to combine ML decoders with a competing discriminator network that tries to distinguish between codewords and noisy words. Our framework is game-theoretic, motivated by generative adversarial networks (GANs)
  arXiv  Detail & Related papers  (2021-12-21T19:14:44Z)
• Conditional Entropy Coding for Efficient Video Compression [82.35389813794372]
  We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. We first show that a simple architecture modeling the entropy between the image latent codes is as competitive as other neural video compression works and video codecs. We then propose a novel internal learning extension on top of this architecture that brings an additional 10% savings without trading off decoding speed.
  arXiv  Detail & Related papers  (2020-08-20T20:01:59Z)
• Variable Rate Video Compression using a Hybrid Recurrent Convolutional Learning Framework [1.9290392443571382]
  This paper presents PredEncoder, a hybrid video compression framework based on the concept of predictive auto-encoding. A variable-rate block encoding scheme has been proposed in the paper that leads to remarkably high quality to bit-rate ratios.
  arXiv  Detail & Related papers  (2020-04-08T20:49:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.