Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering

• URL: http://arxiv.org/abs/2206.10618v1
• Date: Tue, 21 Jun 2022 09:34:29 GMT
• Title: Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering
• Authors: Haisheng Fu, Feng Liang, Jie Liang, Binglin Li, Guohe Zhang, Jingning Han
• Abstract summary: 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.
• Score: 15.056672221375104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep learning-based image compression has made signifcant progresses, and has achieved better ratedistortion (R-D) performance than the latest traditional method, H.266/VVC, in both subjective metric and the more challenging objective metric. However, a major problem is that many leading learned schemes cannot maintain a good trade-off between performance and complexity. In this paper, we propose an effcient and effective image coding framework, which achieves similar R-D performance with lower complexity than the state of the art. First, we develop an improved multi-scale residual block (MSRB) that can expand the receptive feld and is easier to obtain global information. It can further capture and reduce the spatial correlation of the latent representations. Second, a more advanced importance map network is introduced to adaptively allocate bits to different regions of the image. Third, we apply a 2D post-quantization flter (PQF) to reduce the quantization error, motivated by the Sample Adaptive Offset (SAO) flter in video coding. Moreover, We fnd that the complexity of encoder and decoder have different effects on image compression performance. Based on this observation, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only needs one stage of MSRB to yield satisfactory reconstruction, thereby reducing the decoding complexity without sacrifcing performance. Experimental results show that compared to the state-of-the-art method, the encoding and decoding time of the proposed method are about 17 times faster, and the R-D performance is only reduced by less than 1% on both Kodak and Tecnick datasets, which is still better than H.266/VVC(4:4:4) and other recent learning-based methods. Our source code is publicly available at https://github.com/fengyurenpingsheng.

Related papers

• Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression [58.618625678054826]
  This study presents an enhanced neural compression method designed for optimal visual fidelity. We have trained our model with a sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual loss, style loss, and a non-binary adversarial loss. Our empirical findings demonstrate that this approach significantly improves the statistical fidelity of neural image compression.
  arXiv  Detail & Related papers  (2024-01-25T08:11:27Z)
• An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding [43.43996899487615]
  Implicit Neural Representation (INR) for image compression is an emerging technology that offers two key benefits compared to cutting-edge autoencoder models. We introduce a new Mixed AutoRegressive Model (MARM) to significantly reduce the decoding time for the current INR. MARM includes our proposed AutoRegressive Upsampler (ARU) blocks, which are highly efficient, and ARM from previous work to balance decoding time and reconstruction quality.
  arXiv  Detail & Related papers  (2024-01-23T09:37:58Z)
• 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)
• Closed-Loop Transcription via Convolutional Sparse Coding [29.75613581643052]
  Autoencoders often use generic deep networks as the encoder or decoder, which are difficult to interpret. In this work, we make the explicit assumption that the image distribution is generated from a multistage convolution sparse coding (CSC) Our method enjoys several side benefits, including more structured and interpretable representations, more stable convergence, and scalability to large datasets.
  arXiv  Detail & Related papers  (2023-02-18T14:40:07Z)
• Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising [50.039949798156826]
  This paper tackles the challenging problem of hyperspectral (HS) image denoising. We propose rank-enhanced low-dimensional convolution set (Re-ConvSet) We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method.
  arXiv  Detail & Related papers  (2022-07-09T13:35:12Z)
• Reducing Redundancy in the Bottleneck Representation of the Autoencoders [98.78384185493624]
  Autoencoders are a type of unsupervised neural networks, which can be used to solve various tasks. We propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
  arXiv  Detail & Related papers  (2022-02-09T18:48:02Z)
• Modeling Image Quantization Tradeoffs for Optimal Compression [0.0]
  Lossy compression algorithms target tradeoffs by quantizating high frequency data to increase compression rates. We propose a new method of optimizing quantization tables using Deep Learning and a minimax loss function.
  arXiv  Detail & Related papers  (2021-12-14T07:35:22Z)
• Implicit Neural Representations for Image Compression [103.78615661013623]
  Implicit Neural Representations (INRs) have gained attention as a novel and effective representation for various data types. We propose the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding. We find that our approach to source compression with INRs vastly outperforms similar prior work.
  arXiv  Detail & Related papers  (2021-12-08T13:02:53Z)
• Checkerboard Context Model for Efficient Learned Image Compression [6.376339829493938]
  For learned image compression, the autoregressive context model is proved effective in improving the rate-distortion (RD) performance. We propose a parallelizable checkerboard context model (CCM) to solve the problem. Speeding up the decoding process more than 40 times in our experiments, it significantly improved computational efficiency with almost the same rate-distortion performance.
  arXiv  Detail & Related papers  (2021-03-29T03:25:41Z)
• 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)
• 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.