Deep Learning-based Image Compression with Trellis Coded Quantization
- URL: http://arxiv.org/abs/2001.09417v1
- Date: Sun, 26 Jan 2020 08:00:04 GMT
- Title: Deep Learning-based Image Compression with Trellis Coded Quantization
- Authors: Binglin Li, Mohammad Akbari, Jie Liang, Yang Wang
- Abstract summary: We propose to incorporate trellis coded quantizer (TCQ) into a deep learning based image compression framework.
A soft-to-hard strategy is applied to allow for back propagation during training.
We develop a simple image compression model that consists of threeworks (encoder, decoder and entropy estimation) and optimize all of the components in an end-to-end manner.
- Score: 13.728517700074423
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently many works attempt to develop image compression models based on deep
learning architectures, where the uniform scalar quantizer (SQ) is commonly
applied to the feature maps between the encoder and decoder. In this paper, we
propose to incorporate trellis coded quantizer (TCQ) into a deep learning based
image compression framework. A soft-to-hard strategy is applied to allow for
back propagation during training. We develop a simple image compression model
that consists of three subnetworks (encoder, decoder and entropy estimation),
and optimize all of the components in an end-to-end manner. We experiment on
two high resolution image datasets and both show that our model can achieve
superior performance at low bit rates. We also show the comparisons between TCQ
and SQ based on our proposed baseline model and demonstrate the advantage of
TCQ.
Related papers
- Lossy Image Compression with Quantized Hierarchical VAEs [33.173021636656465]
ResNet VAEs are originally designed for data (image) distribution modeling.
We present a powerful and efficient model that outperforms previous methods on natural image lossy compression.
Our model compresses images in a coarse-to-fine fashion and supports parallel encoding and decoding.
arXiv Detail & Related papers (2022-08-27T17:15:38Z) - 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) - Estimating the Resize Parameter in End-to-end Learned Image Compression [50.20567320015102]
We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models.
Our results show that our new resizing parameter estimation framework can provide Bjontegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
arXiv Detail & Related papers (2022-04-26T01:35:02Z) - 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) - 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) - Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model
and Concatenated Residual Modules [22.818632387206257]
Two key components of learned image compression are the entropy model of the latent representations and the encoding/decoding network architectures.
We propose a more flexible discretized Gaussian-Laplacian-Logistic mixture model (GLLMM) for the latent representations.
In the encoding/decoding network design part, we propose a residual blocks (CRB) where multiple residual blocks are serially connected with additional shortcut connections.
arXiv Detail & Related papers (2021-07-14T02:54:22Z) - 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) - Quantization Guided JPEG Artifact Correction [69.04777875711646]
We develop a novel architecture for artifact correction using the JPEG files quantization matrix.
This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings.
arXiv Detail & Related papers (2020-04-17T00:10:08Z) - Learning End-to-End Lossy Image Compression: A Benchmark [90.35363142246806]
We first conduct a comprehensive literature survey of learned image compression methods.
We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes.
By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance.
arXiv Detail & Related papers (2020-02-10T13:13:43Z) - 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.