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Towards learning-based image compression for storage on DNA support

Publications associées (52)

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Towards super resolution in the compressed domain of learning-based image codecs

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

Learning-based image coding has shown promising results during recent years. Unlike the traditional approaches to image compression, learning-based codecs exploit deep neural networks for reducing dimensionality of the input at the stage where a linear tra ...

SPIE2021

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

Dan Alistarh, Fartash Faghri

As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training. One popular communication-compression method for data-para ...

MICROTOME PUBL2021

Performance Evaluation of Objective Image Quality Metrics on Conventional and Learning-Based Compression Artifacts

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

Lossy image compression is a popular, simple and effective solution to reduce the amount of data representing digital pictures. In most lossy compression methods, the reduced volume of data in bits is achieved at the expense of introducing visual artifacts ...

2021

Towards image denoising in the latent space of learning-based compression

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

In recent years, learning-based image compression has demonstrated similar or superior performance when com- pared to conventional approaches in terms of compression efficiency and visual quality. Typically, learning-based image compression takes advantage ...

SPIE2021

Image Compression and Quality Assessment using Convolutional Neural Networks

Pinar Akyazi

The rapid development of digital imaging and video has placed visual contents in the heart of our lives. Digital multimedia span a vast number of areas from business to leisure, including but not limited to education, medicine, accessibility, training, adv ...

EPFL2020

(Geometry Aware) Deep Learning-based Omnidirectional Image Compression

Yamin Sepehri

Omnidirectional images are the spherical visual signals that provide a wide, 360◦, view of a scene from a specific position. Such images are becoming increasingly popular in fields like virtual reality and robotics. Compared to conventional 2D images, the ...

2020

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Point cloud imaging has emerged as an efficient and popular solution to represent immersive visual information. However, the large volume of data generated in the acquisition process reveals the need of efficient compression solutions in order to store and ...

2020

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Computer generated optical volume elements have been investigated for information storage, spectral filtering, and imaging applications. Advancements in additive manufacturing (3D printing) allow the fabrication of multilayered diffractive volume elements ...

2020

Recently deep learning based image compression has made rapid advances with promising results based on objective quality metrics. However, a rigorous subjective quality evaluation on such compression schemes have rarely been reported. This paper aims at pe ...

IEEE2019

Performance assessment of the BTDF data compression based on wavelet transforms in daylighting simulation

Jean-Louis Scartezzini, Jérôme Henri Kämpf, Yujie Wu

The bidirectional transmittance distribution function (BTDF), which is used to characterize the light transmission of a complex fenestration system (CFS), commonly involves bulky volume of data that can be a challenge to data storage and transmission in li ...

2019