Optimized GPU Implementation of JPEG 2000 for Satellite Image Decompression

Date

2018-10-31

Author

UFUK, derviş utku
Temizel, Alptekin
ÖZBAYOĞLU, AHMET MURAT

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

65
views

0
downloads

JPEG 2000 is a powerful yet computationally complex image compression algorithm which is widely used in remote sensing applications. In this work, we focus on speeding-up the decompression algorithm by applying various GPU optimization techniques. We have conducted numerous experiments on high-resolution satellite images in two operational modes; a synchronous mode and an asynchronous batch mode. We share our experiment results and make performance evaluations regarding each operational mode and optimization method separately. Finally we propose an optimized GPU architecture for satellite image decompression and compare the achieved performance with a multi-threaded CPU architecture.

Subject Keywords

JPEG 2000, GPU, CUDA, Image compression, Parallel processing, Satellite imagery

URI

https://hdl.handle.net/11511/31919

DOI

https://doi.org/10.1109/cse.2018.00014

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

A real-time, low-latency, FPGA implementation of the two dimensional discrete wavelet transform Benderli, Oğuz; Tekmen, Yusuf Çağatay; Department of Electrical and Electronics Engineering (2003) This thesis presents an architecture and an FPGA implementation of the two dimensional discrete wavelet transformation (DWT) for applications where row-based raw image data is streamed in at high bandwidths and local buffering of the entire image is not feasible. The architecture is especially suited for multi-spectral imager systems, such as on board an imaging satellite, however can be used in any application where time to next image constraints require real-time processing of multiple images. The latency...
End-to-end learned image compression with conditional latent space modelling for entropy coding Yeşilyurt, Aziz Berkay; Kamışlı, Fatih; Department of Electrical and Electronics Engineering (2019) This thesis presents a lossy image compression system based on an end-to-end trainable neural network. Traditional compression algorithms use linear transformation, quantization and entropy coding steps that are designed based on simple models of the data and are aimed to be low complexity. In neural network based image compression methods, the processing steps, such as transformation and entropy coding, are performed using neural networks. The use of neural networks enables transforms or probability models...
Image compression method based on learned lifting-based dwt and learned zerotree-like entropy model Şahin, Uğur Berk; Kamışlı, Fatih; Department of Electrical and Electronics Engineering (2022-8) The success of deep learning in computer vision has sparked great interest in investigating deep learning-based algorithms also in many image processing applications, including image compression. The most popular end-to-end learned image compression approaches are based on auto-encoder architectures, where the image is mapped via convolutional neural networks (CNNs) into a transform (latent) representation that is quantized and processed again with CNNs to obtain the reconstructed image. The quantized laten...
A pixel-by-pixel learned lossless image compression method with parallel decoding Gümüş, Sinem; Kamışlı, Fatih; Department of Electrical and Electronics Engineering (2022-7) The success of deep learning in computer vision applications has led to the use of learning based algorithms also in image compression. Learning based lossless image compression algorithms can be divided into three categories, namely, pixel-by-pixel (or masked convolution based) algorithms, prior based algorithms and latent representation based algorithms. In the pixel-by-pixel algorithms, each pixel’s probability distribution is obtained by processing the previously coded left and upper neighbouring pixels...
End-to-end learned image compression with conditional latent space modeling for entropy coding Yesilyurt, Aziz Berkay; Kamışlı, Fatih (2021-01-24) The use of neural networks in image compression enables transforms and probability models for entropy coding which can process images based on much more complex models than the simple Gauss-Markov models in traditional compression methods. All at the expense of higher computational complexity. In the neural-network based image compression literature, various methods to model the dependencies in the transform domain/latent space are proposed. This work uses an alternative method to exploit the dependencies o...

Citation Formats

d. u. UFUK, A. Temizel, and A. M. ÖZBAYOĞLU, “Optimized GPU Implementation of JPEG 2000 for Satellite Image Decompression,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31919.