GPU-Oriented Architecture for an End-to-End Image/Video Codec Based on JPEG2000

Cea-Dominguez, Carlos de; Moure, Juan Carlos; Bartrina-Rapesta, Joan; Aulí-Llinàs, Francesc

doi:10.1109/access.2020.2985859

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…In order to perform processing operations on the given data, the execution threads require memory to work with. The memory architecture of the GPUs is divided into three categories: global memory, shared memory, and local memory [31]. The global memory is in the order of GBs, accessible by all the SM threads and used to store the application data and the expected post-processing results.…”

Section: Gpu Approach For Parallel Computingmentioning

confidence: 99%

GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

Baloi

Belean

Turcu

et al. 2022

Preprint

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The digital era brings up on one hand massive amounts of available data, and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, bio-medical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also evaluated in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of massmedia broadcast materials. Thus, the present paper proposes a GPU based approach for parallel implementation of the Jaro-Winkler string similarity metric computation. Further on, a thresholding-based algorithm is also implemented using GPU for matching records over large datasets. The global GPU RAM memory is used to store multiple string lines as raw data. In the case of a single string, its comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Moreover, based on the computed similarity metrics, an adaptive neural network approach guided by a novelty detection classifier together with a naive neural network implementation are proposed to increase the accuracy of the records matching procedure. Timing considerations and the computational complexity are detailed for the proposed approaches compared with state-of-the-art CPU and GPU approaches. A speed-up factor of 21.6 was obtained for the GPU based JaroWinkler implementation compared with the general purpose processor one, whereas improved accuracy for the records matching procedure was delivered using machine learning approaches.

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Section: Gpu Approach For Parallel Computingmentioning

confidence: 99%

GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

Baloi

Belean

Turcu

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The JPEG compression and coding part adopts THE JPEG special codec chip ZR36060, which can realize the image compression and decoding function. In this paper, only its compression and coding function is used [10][11]. ZR36060 is specially designed for computer video acquisition and editing applications, which can easily realize real-time compression and decompression of video signals.…”

Section: ) Compression Coding Modulementioning

confidence: 99%

Denoising Method of Interior Design Image based on Median Filtering Algorithm

Li¹

2022

IJACSA

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Interior design image generation process is prone to the interference of many factors, resulting in the interior design image denoising effect decreases, denoising time increases, so the interior design image denoising method based on median filtering algorithm is proposed. The architecture of interior design image collection is set up, including video signal conversion module, compression coding module, programmable logic chip module and power module. The interior design image collection is realized by using sensors to collect interior design related video information and converting video signals. Based on the results of image acquisition, the median filtering algorithm based on rough set theory is used to realize the denoising of interior design images. Experimental results show that the denoising effect of the proposed method is better, the average signal-to-noise ratio of interior design images is 54.6dB, and the denoising time is always lower than 0.3s, which can be widely used in practice.

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GPU-based similarity metrics computation and machine learning approaches for string similarity evaluation in large datasets

et al. 2023

View full text Add to dashboard Cite

The digital era brings up on one hand massive amounts of available data and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, biomedical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also used in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of mass-media broadcast materials. The present paper proposes a novel GPU-based approach for parallel implementation of the Jaro–Winkler string similarity metric computation, broadly used for matching strings over large datasets. The proposed implementation is applied in musical industry for matching playlist with over 100k records with a given repertory which includes a collection of over 1 million right owner records. The global GPU RAM memory is used to store multiple string lines representing repertory records, whereas single playlist string comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Further on, the accuracy of the Jaro–Winkler approach for the string matching procedure is increased using both an adaptive neural network approach guided by a novelty detection classifier (aNN) and a multiple-features neural network implementation (MF-NN). Thus, the aNN approach yielded an accuracy of 92% while the MF-NN approach achieved an accuracy of 99% at the cost of increased computational complexity. Timing considerations and the computational complexity are detailed for the proposed approaches compared with both the general-purpose processor (CPU) implementation and the state-of-the-art GPU approaches. A speed-up factor of 21.6 was obtained for the GPU-based Jaro–Winkler implementation compared with the CPU one, whereas a factor of 3.72 was obtained compared with the existing GPU implementation of string matching procedure based on Levenstein distance metrics.

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GPU-Oriented Architecture for an End-to-End Image/Video Codec Based on JPEG2000

Cited by 11 publications

References 26 publications

GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

Denoising Method of Interior Design Image based on Median Filtering Algorithm

GPU-based similarity metrics computation and machine learning approaches for string similarity evaluation in large datasets

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