Mar 6, 2023 � In this research, we first show that INR based image codec has a lower complexity than VAE based approaches, then we propose several improvements for INR-based�...

Our work focuses on this paradigm to improve INR based image compression method. INR represents an image by over-fitting a continuous function (neural network).

Mar 6, 2023 � In this paper, we introduce a novel 8-point DCT approximation that requires only 14 addition operations and no multiplications. The proposed�...

Implicit Neural Representation (INR) for image compression is an emerging technology that offers two key benefits compared to cutting-edge autoencoder models:�...

Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression. An image can be compressed by�...

INRs are a way to represent coordinate based data as a function. For example, an image is nothing else but a function f(x,y) = (R,G,B).

This work proposes the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding.

Extensive experimental results demonstrate that our approach significantly the compression performance of INR-based image compression,�...

Notably, our study pioneers the application of INR-based compression methods to four-dimensional biomedical data. Additionally, we present a novel INR-based�...

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