Joint Image Compression and Denoising via Latent-Space Scalability
- URL: http://arxiv.org/abs/2205.01874v1
- Date: Wed, 4 May 2022 03:29:50 GMT
- Title: Joint Image Compression and Denoising via Latent-Space Scalability
- Authors: Saeed Ranjbar Alvar, Mateen Ulhaq, Hyomin Choi, and Ivan V. Baji\'c
- Abstract summary: We present a learnt image compression framework where image denoising and compression are performed jointly.
The proposed is compared against established compression and denoising benchmarks, and the experiments reveal considerable savings of up to 80%.
- Score: 36.5211475555805
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: When it comes to image compression in digital cameras, denoising is
traditionally performed prior to compression. However, there are applications
where image noise may be necessary to demonstrate the trustworthiness of the
image, such as court evidence and image forensics. This means that noise itself
needs to be coded, in addition to the clean image itself. In this paper, we
present a learnt image compression framework where image denoising and
compression are performed jointly. The latent space of the image codec is
organized in a scalable manner such that the clean image can be decoded from a
subset of the latent space at a lower rate, while the noisy image is decoded
from the full latent space at a higher rate. The proposed codec is compared
against established compression and denoising benchmarks, and the experiments
reveal considerable bitrate savings of up to 80% compared to cascade
compression and denoising.
Related papers
- Enhancing the Rate-Distortion-Perception Flexibility of Learned Image
Codecs with Conditional Diffusion Decoders [7.485128109817576]
We show that conditional diffusion models can lead to promising results in the generative compression task when used as a decoder.
In this paper, we show that conditional diffusion models can lead to promising results in the generative compression task when used as a decoder.
arXiv Detail & Related papers (2024-03-05T11:48:35Z) - MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model [78.4051835615796]
This paper proposes a method called Multimodal Image Semantic Compression.
It consists of an LMM encoder for extracting the semantic information of the image, a map encoder to locate the region corresponding to the semantic, an image encoder generates an extremely compressed bitstream, and a decoder reconstructs the image based on the above information.
It can achieve optimal consistency and perception results while saving perceptual 50%, which has strong potential applications in the next generation of storage and communication.
arXiv Detail & Related papers (2024-02-26T17:11:11Z) - Joint End-to-End Image Compression and Denoising: Leveraging Contrastive
Learning and Multi-Scale Self-ONNs [18.71504105967766]
Noisy images are a challenge to image compression algorithms due to the inherent difficulty of compressing noise.
We propose a novel method integrating a multi-scale denoiser comprising of Self Organizing Operational Neural Networks, for joint image compression and denoising.
arXiv Detail & Related papers (2024-02-08T11:33:16Z) - FLLIC: Functionally Lossless Image Compression [16.892815659154053]
We propose a new paradigm of joint denoising and compression called functionally lossless image compression (FLLIC)
FLLIC achieves state-of-the-art performance in joint denoising and compression of noisy images and does so at a lower computational cost.
arXiv Detail & Related papers (2024-01-24T17:44:33Z) - 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) - Optimizing Image Compression via Joint Learning with Denoising [49.83680496296047]
High levels of noise usually exist in today's captured images due to the relatively small sensors equipped in the smartphone cameras.
We propose a novel two-branch, weight-sharing architecture with plug-in feature denoisers to allow a simple and effective realization of the goal with little computational cost.
arXiv Detail & Related papers (2022-07-22T04:23:01Z) - Analysis of the Effect of Low-Overhead Lossy Image Compression on the
Performance of Visual Crowd Counting for Smart City Applications [78.55896581882595]
Lossy image compression techniques can reduce the quality of the images, leading to accuracy degradation.
In this paper, we analyze the effect of applying low-overhead lossy image compression methods on the accuracy of visual crowd counting.
arXiv Detail & Related papers (2022-07-20T19:20:03Z) - Soft Compression for Lossless Image Coding [17.714164324169037]
We propose a new concept, compressible indicator function with regard to image.
It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images can be greatly reduced by applying soft compression.
arXiv Detail & Related papers (2020-12-11T10:59:47Z) - 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.