Adapting Learned Image Codecs to Screen Content via Adjustable Transformations

• URL: http://arxiv.org/abs/2402.17544v1
• Date: Tue, 27 Feb 2024 14:34:14 GMT
• Title: Adapting Learned Image Codecs to Screen Content via Adjustable Transformations
• Authors: H. Burak Dogaroglu, A. Burakhan Koyuncu, Atanas Boev, Elena Alshina, Eckehard Steinbach
• Abstract summary: We propose to introduce parameterized and invertible linear transformations into the coding pipeline without changing the underlying baseline's operation flow. Our end-to-end trained solution achieves up to 10% savings on SC compression compared to the baseline LICs.
• Score: 1.9249287163937978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As learned image codecs (LICs) become more prevalent, their low coding efficiency for out-of-distribution data becomes a bottleneck for some applications. To improve the performance of LICs for screen content (SC) images without breaking backwards compatibility, we propose to introduce parameterized and invertible linear transformations into the coding pipeline without changing the underlying baseline codec's operation flow. We design two neural networks to act as prefilters and postfilters in our setup to increase the coding efficiency and help with the recovery from coding artifacts. Our end-to-end trained solution achieves up to 10% bitrate savings on SC compression compared to the baseline LICs while introducing only 1% extra parameters.

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)
• HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression [51.04820313355164]
  HyrbidFlow combines the continuous-feature-based and codebook-based streams to achieve both high perceptual quality and high fidelity under extreme lows. Experimental results demonstrate superior performance across several datasets under extremely lows.
  arXiv  Detail & Related papers  (2024-04-20T13:19:08Z)
• Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression [60.689646881479064]
  We propose a progressive learning paradigm for transformer-based variable-rate image compression. Inspired by visual prompt tuning, we use LPM to extract prompts for input images and hidden features at the encoder side and decoder side, respectively. Our model outperforms all current variable image methods in terms of rate-distortion performance and approaches the state-of-the-art fixed image compression methods trained from scratch.
  arXiv  Detail & Related papers  (2023-11-23T08:29:32Z)
• Neural Image Compression Using Masked Sparse Visual Representation [17.229601298529825]
  We study neural image compression based on the Sparse Visual Representation (SVR), where images are embedded into a discrete latent space spanned by learned visual codebooks. By sharing codebooks with the decoder, the encoder transfers codeword indices that are efficient and cross-platform robust. We propose a Masked Adaptive Codebook learning (M-AdaCode) method that applies masks to the latent feature subspace to balance and reconstruction quality.
  arXiv  Detail & Related papers  (2023-09-20T21:59:23Z)
• Neural Data-Dependent Transform for Learned Image Compression [72.86505042102155]
  We build a neural data-dependent transform and introduce a continuous online mode decision mechanism to jointly optimize the coding efficiency for each individual image. The experimental results show the effectiveness of the proposed neural-syntax design and the continuous online mode decision mechanism.
  arXiv  Detail & Related papers  (2022-03-09T14:56:48Z)
• Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder [73.48927855855219]
  We propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends. Experiments demonstrate that our approach successfully improves the rate-distortion performance over JPEG across various quality metrics.
  arXiv  Detail & Related papers  (2022-01-27T20:20:03Z)
• DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression [22.933872281183497]
  We propose the first learned fine-grained scalable image compression model (DeepFGS) In this paper, we introduce a feature separation backbone to divide the image information into basic and scalable features, then redistribute the features channel by channel through an information rearrangement strategy. Experiments demonstrate that our DeepFGS outperforms all learning-based scalable image compression models and conventional scalable image codecs in PSNR and MS-SSIM metrics.
  arXiv  Detail & Related papers  (2022-01-04T15:03:13Z)
• Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks [15.308823742699039]
  We propose a new variable-rate image compression framework, which employs generalized octave convolutions (GoConv) and generalized octave transposed-convolutions (GoTConv) To enable a single model to operate with different bit rates and to learn multi-rate image features, a new objective function is introduced. Experimental results show that the proposed framework trained with variable-rate objective function outperforms the standard codecs such as H.265/HEVC-based BPG and state-of-the-art learning-based variable-rate methods.
  arXiv  Detail & Related papers  (2020-12-31T06:26:56Z)
• Modeling Lost Information in Lossy Image Compression [72.69327382643549]
  Lossy image compression is one of the most commonly used operators for digital images. We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
  arXiv  Detail & Related papers  (2020-06-22T04:04:56Z)
• Content Adaptive and Error Propagation Aware Deep Video Compression [110.31693187153084]
  We propose a content adaptive and error propagation aware video compression system. Our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system.
  arXiv  Detail & Related papers  (2020-03-25T09:04:24Z)

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.