Deep Perceptual Image Quality Assessment for Compression
- URL: http://arxiv.org/abs/2103.01114v1
- Date: Mon, 1 Mar 2021 16:31:10 GMT
- Title: Deep Perceptual Image Quality Assessment for Compression
- Authors: Juan Carlos Mier, Eddie Huang, Hossein Talebi, Feng Yang, Peyman
Milanfar
- Abstract summary: We propose the largest image compression quality dataset to date with human perceptual preferences.
We show that the proposed model can effectively learn from thousands of examples available in the new dataset.
- Score: 12.868773445948507
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Lossy Image compression is necessary for efficient storage and transfer of
data. Typically the trade-off between bit-rate and quality determines the
optimal compression level. This makes the image quality metric an integral part
of any imaging system. While the existing full-reference metrics such as PSNR
and SSIM may be less sensitive to perceptual quality, the recently introduced
learning methods may fail to generalize to unseen data. In this paper we
propose the largest image compression quality dataset to date with human
perceptual preferences, enabling the use of deep learning, and we develop a
full reference perceptual quality assessment metric for lossy image compression
that outperforms the existing state-of-the-art methods. We show that the
proposed model can effectively learn from thousands of examples available in
the new dataset, and consequently it generalizes better to other unseen
datasets of human perceptual preference.
Related papers
- Transferable Learned Image Compression-Resistant Adversarial Perturbations [66.46470251521947]
Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks.
We introduce a new pipeline that targets image classification models that utilize learned image compressors as pre-processing modules.
arXiv Detail & Related papers (2024-01-06T03:03:28Z) - HFLIC: Human Friendly Perceptual Learned Image Compression with
Reinforced Transform [16.173583505483272]
Current learning-based image compression methods often sacrifice human-friendly compression and require long decoding times.
We propose enhancements to the backbone network and loss function of existing image compression model, focusing on improving human perception and efficiency.
arXiv Detail & Related papers (2023-05-12T14:35:27Z) - Machine Perception-Driven Image Compression: A Layered Generative
Approach [32.23554195427311]
layered generative image compression model is proposed to achieve high human vision-oriented image reconstructed quality.
Task-agnostic learning-based compression model is proposed, which effectively supports various compressed domain-based analytical tasks.
Joint optimization schedule is adopted to acquire best balance point among compression ratio, reconstructed image quality, and downstream perception performance.
arXiv Detail & Related papers (2023-04-14T02:12:38Z) - Perceptual Quality Assessment for Fine-Grained Compressed Images [38.615746092795625]
We propose a full-reference image quality assessment (FR-IQA) method for compressed images of fine-grained levels.
The proposed method is validated on the fine-grained compression image quality assessment (FGIQA) database.
arXiv Detail & Related papers (2022-06-08T12:56:45Z) - 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) - 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) - Analyzing and Mitigating JPEG Compression Defects in Deep Learning [69.04777875711646]
We present a unified study of the effects of JPEG compression on a range of common tasks and datasets.
We show that there is a significant penalty on common performance metrics for high compression.
arXiv Detail & Related papers (2020-11-17T20:32:57Z) - Perceptually Optimizing Deep Image Compression [53.705543593594285]
Mean squared error (MSE) and $ell_p$ norms have largely dominated the measurement of loss in neural networks.
We propose a different proxy approach to optimize image analysis networks against quantitative perceptual models.
arXiv Detail & Related papers (2020-07-03T14:33:28Z) - Early Exit or Not: Resource-Efficient Blind Quality Enhancement for
Compressed Images [54.40852143927333]
Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts.
We propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images.
Our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images.
arXiv Detail & Related papers (2020-06-30T07:38:47Z) - Saliency Driven Perceptual Image Compression [6.201592931432016]
The paper demonstrates that the popularly used evaluations metrics such as MS-SSIM and PSNR are inadequate for judging the performance of image compression techniques.
A new metric is proposed, which is learned on perceptual similarity data specific to image compression.
The model not only generates images which are visually better but also gives superior performance for subsequent computer vision tasks.
arXiv Detail & Related papers (2020-02-12T13:43:17Z)
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.