Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
- URL: http://arxiv.org/abs/2402.17487v1
- Date: Tue, 27 Feb 2024 13:12:18 GMT
- Title: Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
- Authors: Panqi Jia, A. Burakhan Koyuncu, Jue Mao, Ze Cui, Yi Ma, Tiansheng Guo,
Timofey Solovyev, Alexander Karabutov, Yin Zhao, Jing Wang, Elena Alshina,
Andre Kaup
- Abstract summary: The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks.
The current state of the JPEG-AI verification model experiences significant slowdowns during bit rate matching.
The proposed methodology offers a fourfold acceleration and over 1% improvement in BD-rate at the base operation point.
- Score: 45.64945099984195
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The research on neural network (NN) based image compression has shown
superior performance compared to classical compression frameworks. Unlike the
hand-engineered transforms in the classical frameworks, NN-based models learn
the non-linear transforms providing more compact bit representations, and
achieve faster coding speed on parallel devices over their classical
counterparts. Those properties evoked the attention of both scientific and
industrial communities, resulting in the standardization activity JPEG-AI. The
verification model for the standardization process of JPEG-AI is already in
development and has surpassed the advanced VVC intra codec. To generate
reconstructed images with the desired bits per pixel and assess the BD-rate
performance of both the JPEG-AI verification model and VVC intra, bit rate
matching is employed. However, the current state of the JPEG-AI verification
model experiences significant slowdowns during bit rate matching, resulting in
suboptimal performance due to an unsuitable model. The proposed methodology
offers a gradual algorithmic optimization for matching bit rates, resulting in
a fourfold acceleration and over 1% improvement in BD-rate at the base
operation point. At the high operation point, the acceleration increases up to
sixfold.
Related papers
- Bit Distribution Study and Implementation of Spatial Quality Map in the
JPEG-AI Standardization [39.71764233394706]
The JPEG-AI verification model has been released and is currently under development for standardization.
We propose a spatial bit allocation method to optimize the JPEG-AI verification model's bit distribution and enhance the visual quality.
arXiv Detail & Related papers (2024-02-27T12:52:44Z) - ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image
Compression [18.05997169440533]
We propose ConvNeXt-ChARM, an efficient ConvNeXt-based transform coding framework, paired with a compute-efficient channel-wise auto-regressive auto-regressive.
We show that ConvNeXt-ChARM brings consistent and significant BD-rate (PSNR) reductions estimated on average to 5.24% and 1.22% over the versatile video coding (VVC) reference encoder (VTM-18.0) and the state-of-the-art learned image compression method SwinT-ChARM.
arXiv Detail & Related papers (2023-07-12T11:45:54Z) - Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient
Neural Image Compression [11.25130799452367]
We propose an absolute image compression transformer (ICT) for neural image compression (NIC)
ICT captures both global and local contexts from the latent representations and better parameterize the distribution of the quantized latents.
Our framework significantly improves the trade-off between coding efficiency and decoder complexity over the versatile video coding (VVC) reference encoder (VTM-18.0) and the neural SwinT-ChARM.
arXiv Detail & Related papers (2023-07-05T13:17:14Z) - 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) - An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy
Image Compression Systems [73.48927855855219]
Recent advances in deep learning have resulted in image compression algorithms that outperform JPEG and JPEG 2000 on the standard Kodak benchmark.
In this paper, we perform the first large-scale comparison of recent state-of-the-art hybrid neural compression algorithms.
arXiv Detail & Related papers (2022-01-27T19:47:51Z) - Learning to Improve Image Compression without Changing the Standard
Decoder [100.32492297717056]
We propose learning to improve the encoding performance with the standard decoder.
Specifically, a frequency-domain pre-editing method is proposed to optimize the distribution of DCT coefficients.
We do not modify the JPEG decoder and therefore our approach is applicable when viewing images with the widely used standard JPEG decoder.
arXiv Detail & Related papers (2020-09-27T19:24:42Z) - 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)
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.