Dynamic Multiple Description Wavelet Based Image Coding Using Enhanced Particle Swarm Optimization

Kusetoğulları, Hüseyin

doi:10.1109/jstsp.2014.2331911

Cited by 6 publications

(2 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to P N×D , computing updated particle positions requires the velocities V N×D , resulting in a large amount of memory transferred to the GPU global memory and back again. Although the moving peaks dynamic cost function did not require a large amount of external data, many realistic dynamic problems do [3,5]. Finally, cache size also has an effect on performance.…”

Section: Speedupmentioning

confidence: 99%

Dynamic particle swarm optimization with heterogeneous multicore parallelism and GPU acceleration

Wachowiak

Wachowiak-Smolikova

Rotondo

2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

View full text Add to dashboard Cite

Global optimization of dynamic cost functions is important in many engineering applications. For these tasks, global optima change over time, or are greatly affected by dynamic noise. Nature-based stochastic methods, including genetic algorithms, particle swarm optimization (PSO), and differential evolution, have been particularly effective in dynamic optimization. However, these methods are generally very computationally intensive, and consequently research has focused on parallelization paradigms. In this paper, PSO approaches for dynamic optimization are analyzed for parallelization opportunities on relatively inexpensive, readilyavailable heterogeneous parallel graphics processing unit (GPU) and multicore hardware. A sophisticated adaptation of PSO-a multi-swarm technique proposed for dynamic problems-is parallelized in different ways at multiple levels. Experimental results on high-dimensional "moving-peaks" functions show that high speedups can be obtained through making use of different high-performance components on commodity hardware. Heterogeneous high-performance computing is proposed as a way to mitigate the time complexity of dynamic PSO adaptions.

show abstract

Section: Speedupmentioning

confidence: 99%

Dynamic particle swarm optimization with heterogeneous multicore parallelism and GPU acceleration

Wachowiak

Wachowiak-Smolikova

Rotondo

2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

View full text Add to dashboard Cite

show abstract

“…This strategy capitalizes on the computational advantages of FFT to accelerate the CS process while maintaining reconstruction fidelity. While many approaches can improve compression efficiency at the encoding stage [ 25 , 26 , 27 , 28 ], they are not necessarily applicable or effective for QCS.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Quantization Method for Block Compressed Sensing of Images

Gong,

Chen,

Zhu

et al. 2024

Entropy

View full text Add to dashboard Cite

Block compressed sensing (BCS) is a promising method for resource-constrained image/video coding applications. However, the quantization of BCS measurements has posed a challenge, leading to significant quantization errors and encoding redundancy. In this paper, we propose a quantization method for BCS measurements using convolutional neural networks (CNN). The quantization process maps measurements to quantized data that follow a uniform distribution based on the measurements’ distribution, which aims to maximize the amount of information carried by the quantized data. The dequantization process restores the quantized data to data that conform to the measurements’ distribution. The restored data are then modified by the correlation information of the measurements drawn from the quantized data, with the goal of minimizing the quantization errors. The proposed method uses CNNs to construct quantization and dequantization processes, and the networks are trained jointly. The distribution parameters of each block are used as side information, which is quantized with 1 bit by the same method. Extensive experiments on four public datasets showed that, compared with uniform quantization and entropy coding, the proposed method can improve the PSNR by an average of 0.48 dB without using entropy coding when the compression bit rate is 0.1 bpp.

show abstract

Distributed sub-Nyquist sampling and parameters measurement of common frequency support MEDS signals

Huang,

Deng,

et al. 2023

Digital Signal Processing

View full text Add to dashboard Cite

Dynamic Multiple Description Wavelet Based Image Coding Using Enhanced Particle Swarm Optimization

Cited by 6 publications

References 62 publications

Dynamic particle swarm optimization with heterogeneous multicore parallelism and GPU acceleration

Dynamic particle swarm optimization with heterogeneous multicore parallelism and GPU acceleration

A Convolutional Neural Network-Based Quantization Method for Block Compressed Sensing of Images

Distributed sub-Nyquist sampling and parameters measurement of common frequency support MEDS signals

Contact Info

Product

Resources

About