PatchSVD: A Non-uniform SVD-based Image Compression Algorithm
- URL: http://arxiv.org/abs/2406.05129v1
- Date: Fri, 7 Jun 2024 17:57:40 GMT
- Title: PatchSVD: A Non-uniform SVD-based Image Compression Algorithm
- Authors: Zahra Golpayegani, Nizar Bouguila,
- Abstract summary: We propose a novel region-based lossy image compression technique, called PatchSVD, based on the Singular Value Decomposition (SVD) algorithm.
We show through experiments that PatchSVD outperforms SVD-based image compression with respect to three popular image compression metrics.
- Score: 20.856903918492154
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Storing data is particularly a challenge when dealing with image data which often involves large file sizes due to the high resolution and complexity of images. Efficient image compression algorithms are crucial to better manage data storage costs. In this paper, we propose a novel region-based lossy image compression technique, called PatchSVD, based on the Singular Value Decomposition (SVD) algorithm. We show through experiments that PatchSVD outperforms SVD-based image compression with respect to three popular image compression metrics. Moreover, we compare PatchSVD compression artifacts with those of Joint Photographic Experts Group (JPEG) and SVD-based image compression and illustrate some cases where PatchSVD compression artifacts are preferable compared to JPEG and SVD artifacts.
Related papers
- Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution [33.69980388844034]
In this paper, we show that the commonly used JPEG algorithm is not best suited for further compression.
We propose Stain Quantized Latent Compression, a novel DL based histopathology data compression approach.
We show that our approach yields superior performance in a classification downstream task, compared to traditional approaches like JPEG.
arXiv Detail & Related papers (2024-06-18T13:47:17Z) - 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) - CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless
Compression of High-Color DICOM Medical Images [0.0]
Medical images require a high color depth of 12 bits per pixel component for accurate analysis by physicians.
Standard-based compression of images via filtering is well-known; however, it remains suboptimal in the medical domain due to non-specialized implementations.
This study proposes a medical image compression algorithm, CompaCT, that aims to target spatial features and patterns of pixel concentration for dynamically enhanced data processing.
arXiv Detail & Related papers (2023-08-24T21:43:04Z) - Learned Lossless Compression for JPEG via Frequency-Domain Prediction [50.20577108662153]
We propose a novel framework for learned lossless compression of JPEG images.
To enable learning in the frequency domain, DCT coefficients are partitioned into groups to utilize implicit local redundancy.
An autoencoder-like architecture is designed based on the weight-shared blocks to realize entropy modeling of grouped DCT coefficients.
arXiv Detail & Related papers (2023-03-05T13:15:28Z) - Data-Efficient Sequence-Based Visual Place Recognition with Highly
Compressed JPEG Images [17.847661026367767]
Visual Place Recognition (VPR) is a fundamental task that allows a robotic platform to successfully localise itself in the environment.
JPEG is an image compression standard that can employ high compression ratios to facilitate lower data transmission for VPR applications.
When applying high levels of JPEG compression, both the image clarity and size are drastically reduced.
arXiv Detail & Related papers (2023-02-26T13:13:51Z) - Data Efficient Visual Place Recognition Using Extremely JPEG-Compressed
Images [17.847661026367767]
This paper studies the effects of JPEG compression on the performance of Visual Place Recognition techniques.
We show that by introducing compression, the VPR performance is drastically reduced, especially in the higher spectrum of compression.
We present a fine-tuned CNN which is optimized for JPEG compressed data and show that it performs more consistently with the image transformations detected in extremely compressed JPEG images.
arXiv Detail & Related papers (2022-09-17T14:46:28Z) - Enhanced Invertible Encoding for Learned Image Compression [40.21904131503064]
In this paper, we propose an enhanced Invertible.
Network with invertible neural networks (INNs) to largely mitigate the information loss problem for better compression.
Experimental results on the Kodak, CLIC, and Tecnick datasets show that our method outperforms the existing learned image compression methods.
arXiv Detail & Related papers (2021-08-08T17:32:10Z) - Towards Robust Data Hiding Against (JPEG) Compression: A
Pseudo-Differentiable Deep Learning Approach [78.05383266222285]
It is still an open challenge to achieve the goal of data hiding that can be against these compressions.
Deep learning has shown large success in data hiding, while non-differentiability of JPEG makes it challenging to train a deep pipeline for improving robustness against lossy compression.
In this work, we propose a simple yet effective approach to address all the above limitations at once.
arXiv Detail & Related papers (2020-12-30T12:30:09Z) - 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) - Learning Better Lossless Compression Using Lossy Compression [100.50156325096611]
We leverage the powerful lossy image compression algorithm BPG to build a lossless image compression system.
We model the distribution of the residual with a convolutional neural network-based probabilistic model that is conditioned on the BPG reconstruction.
Finally, the image is stored using the concatenation of the bitstreams produced by BPG and the learned residual coder.
arXiv Detail & Related papers (2020-03-23T11:21:52Z) - Discernible Image Compression [124.08063151879173]
This paper aims to produce compressed images by pursuing both appearance and perceptual consistency.
Based on the encoder-decoder framework, we propose using a pre-trained CNN to extract features of the original and compressed images.
Experiments on benchmarks demonstrate that images compressed by using the proposed method can also be well recognized by subsequent visual recognition and detection models.
arXiv Detail & Related papers (2020-02-17T07:35:08Z)
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.