Leveraging progressive model and overfitting for efficient learned image compression

• URL: http://arxiv.org/abs/2210.04112v1
• Date: Sat, 8 Oct 2022 21:54:58 GMT
• Title: Leveraging progressive model and overfitting for efficient learned image compression
• Authors: Honglei Zhang, Francesco Cricri, Hamed Rezazadegan Tavakoli, Emre Aksu, Miska M. Hannuksela
• Abstract summary: We introduce a powerful and flexible LIC framework with multi-scale progressive (MSP) probability model and latent representation overfitting (LOF) technique. With different predefined profiles, the proposed framework can achieve various balance points between compression efficiency and computational complexity. Experiments show that the proposed framework achieves 2.5%, 1.0%, and 1.3% Bjontegaard delta bit rate (BD-rate) reduction over the VVC/H.266 standard.
• Score: 14.937446839215868
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning is overwhelmingly dominant in the field of computer vision and image/video processing for the last decade. However, for image and video compression, it lags behind the traditional techniques based on discrete cosine transform (DCT) and linear filters. Built on top of an autoencoder architecture, learned image compression (LIC) systems have drawn enormous attention in recent years. Nevertheless, the proposed LIC systems are still inferior to the state-of-the-art traditional techniques, for example, the Versatile Video Coding (VVC/H.266) standard, due to either their compression performance or decoding complexity. Although claimed to outperform the VVC/H.266 on a limited bit rate range, some proposed LIC systems take over 40 seconds to decode a 2K image on a GPU system. In this paper, we introduce a powerful and flexible LIC framework with multi-scale progressive (MSP) probability model and latent representation overfitting (LOF) technique. With different predefined profiles, the proposed framework can achieve various balance points between compression efficiency and computational complexity. Experiments show that the proposed framework achieves 2.5%, 1.0%, and 1.3% Bjontegaard delta bit rate (BD-rate) reduction over the VVC/H.266 standard on three benchmark datasets on a wide bit rate range. More importantly, the decoding complexity is reduced from O(n) to O(1) compared to many other LIC systems, resulting in over 20 times speedup when decoding 2K images.

Related papers

• Accelerating Learned Video Compression via Low-Resolution Representation Learning [18.399027308582596]
  We introduce an efficiency-optimized framework for learned video compression that focuses on low-resolution representation learning. Our method achieves performance levels on par with the low-decay P configuration of the H.266 reference software VTM.
  arXiv  Detail & Related papers  (2024-07-23T12:02:57Z)
• You Can Mask More For Extremely Low-Bitrate Image Compression [80.7692466922499]
  Learned image compression (LIC) methods have experienced significant progress during recent years. LIC methods fail to explicitly explore the image structure and texture components crucial for image compression. We present DA-Mask that samples visible patches based on the structure and texture of original images. We propose a simple yet effective masked compression model (MCM), the first framework that unifies LIC and LIC end-to-end for extremely low-bitrate compression.
  arXiv  Detail & Related papers  (2023-06-27T15:36:22Z)
• Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression [85.93207826513192]
  We propose a unified and powerful deep lossy plus residual (DLPR) coding framework for both lossless and near-lossless image compression. We solve the joint lossy and residual compression problem in the approach of VAEs. In the near-lossless mode, we quantize the original residuals to satisfy a given $ell_infty$ error bound.
  arXiv  Detail & Related papers  (2022-09-11T12:11:56Z)
• Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering [15.056672221375104]
  Deep learning-based image compression has achieved better ratedistortion (R-D) performance than the latest traditional method, H.266/VVC. Many leading learned schemes cannot maintain a good trade-off between performance and complexity. We propose an effcient and effective image coding framework, which achieves similar R-D performance with lower complexity than the state of the art.
  arXiv  Detail & Related papers  (2022-06-21T09:34:29Z)
• Split Hierarchical Variational Compression [21.474095984110622]
  Variational autoencoders (VAEs) have witnessed great success in performing the compression of image datasets. SHVC introduces an efficient autoregressive sub-pixel convolution, that allows a generalisation between per-pixel autoregressions and fully factorised probability models.
  arXiv  Detail & Related papers  (2022-04-05T09:13:38Z)
• Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform [58.60004238261117]
  We propose a versatile deep image compression network based on Spatial Feature Transform (SFT arXiv:1804.02815) Our model covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps. The proposed framework allows us to perform task-aware image compressions for various tasks.
  arXiv  Detail & Related papers  (2021-08-21T17:30:06Z)
• 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)
• Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model [164.7489982837475]
  This paper proposes a Recurrent Learned Video Compression (RLVC) approach with the Recurrent Auto-Encoder (RAE) and Recurrent Probability Model ( RPM) The RAE employs recurrent cells in both the encoder and decoder to exploit the temporal correlation among video frames. Our approach achieves the state-of-the-art learned video compression performance in terms of both PSNR and MS-SSIM.
  arXiv  Detail & Related papers  (2020-06-24T08:46:33Z)
• Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement [164.7489982837475]
  We propose a Hierarchical Learned Video Compression (HLVC) method with three hierarchical quality layers and a recurrent enhancement network. In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides.
  arXiv  Detail & Related papers  (2020-03-04T09:31:37Z)
• Improved Image Coding Autoencoder With Deep Learning [8.92071749364712]
  We build autoencoder based pipelines for extreme end-to-end image compression based on Ball'e's approach. It achieved around 4.0% reduction in bits per pixel (bpp), 0.03% increase in multi-scale structural similarity (MS-SSIM) and only 0.47% decrease in peak signal-to-noise ratio (PSNR)
  arXiv  Detail & Related papers  (2020-02-28T03:21:47Z)
• A Unified End-to-End Framework for Efficient Deep Image Compression [35.156677716140635]
  We propose a unified framework called Efficient Deep Image Compression (EDIC) based on three new technologies. Specifically, we design an auto-encoder style network for learning based image compression. Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.
  arXiv  Detail & Related papers  (2020-02-09T14:21:08Z)

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.