A fast and improved Image Compression technique using Huffman coding

Patel, Rachit; Kumar, Virendra; Tyagi, Vaibhav; Asthana, Vishal

doi:10.1109/wispnet.2016.7566549

Cited by 26 publications

(7 citation statements)

References 2 publications

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“…Once the training phase is completed the image can be compressed in the operational phase. As the hidden layer neurons output represents the encoded data values, it is further encoded using Huffman encoding as a second level of compression [15]. Huffman encoding procedure is explained in Algorithm 3.2.…”

Section: Methodsmentioning

confidence: 99%

Pyramid based Progressive Transmission System with Hybrid Compression Scheme for Medical Images

Ravikiran

Jayanth

2021

SN COMPUT. SCI.

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Transmission of medical images over the internet has become common in telemedicine services. Telemedicine is an enabler for rural health care services where network quality varies over time and there is a need for robustness in presence of poor network quality and efficient use of bandwidth. At the same time, the quality of medical images cannot be compromised with network constraints. In this work, a progressive transmission system with an efficient compression scheme is proposed for the effectual transmission of medical images over the internet with consideration for optimal use of network bandwidth. The image quality can be fine-tuned based on network bandwidth availability and user requirements.

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Section: Methodsmentioning

confidence: 99%

Pyramid based Progressive Transmission System with Hybrid Compression Scheme for Medical Images

Ravikiran

Jayanth

2021

SN COMPUT. SCI.

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“…Firstly, weights of NP-layers are quantified for different levels of quantization (N-levels) using three Q-types including uniform quantization, non-uniform quantization, and asymmetric quantization. In the next step of data compression, Canonical Huffman Coding [32] is applied to previously quantified weights we have obtained for all three Q-types.…”

Section: ) Optimizermentioning

confidence: 99%

Resource-Restricted Environments Based Memory-Efficient Compressed Convolutional Neural Network Model for Image-Level Object Classification

et al. 2023

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In the past decade, Convolutional Neural Networks (CNNs) have achieved tremendous success in solving complex classification problems. CNN architectures require an excessive number of computations to achieve high accuracy. However, these models are deficient due to the heavy cost of storage and energy, which prohibits the application of CNNs to resource-constrained edge-devices. Hence, developing aggressive optimization schemes for efficient deployment of CNNs on edge devices has become the most important requirement. To find the optimal approach, we present a resource-limited environment based memoryefficient network compression model for image-level object classification. The main aim is to compress CNN architecture by achieving low computational cost and memory requirements without dropping system's accuracy. To achieve the said goal, we propose a network compression strategy, that works in a collaborative manner, where Soft Filter Pruning is first applied to reduce the computational cost of the model. In the next step, the model is divided into No-Pruning Layers (NP-Layers) and Pruning Layers (P-Layers). Incremental Quantization is applied to P-Layers due to irregular weights distribution, while for NP-Layers, we propose a novel Optimized Quantization algorithm for the quantization of weights up to optimal levels obtained from the Optimizer. This scheme is designed to achieve the best trade-off between compression ratio and accuracy of the model. Our proposed system is validated for image-level object classification on LeNet-5, CIFARquick, and VGG-16 networks using MNIST, CIFAR-10, and ImageNet ILSVRC2012 datasets respectively. We have achieved high compression ratio with negligible accuracy drop, outperforming the state-of-the-art methods.

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“…The Huffman tree is a data structure that is commonly used for data compression and coding. Later, Huffman trees were also used in other areas such as channel coding (Yin et al, 2021;Liu et al, 2018;Wu et al, 2012), text compression (Dath and Panicker, 2017;Bedruz and Quiros, 2015;Mantoro et al, 2017), image compression (Yuan and Hu, 2019;Kasban and Hashima, 2019;Patel et al, 2016), audio coding (Yi et al, 2019;Yan and Wang, 2011), etc. In recent years, with the development of deep learning, the idea of Huffman trees has been introduced. Morin and Bengio (2005) et al was first proposed using Huffman trees for Hierarchical Softmax, and the method was subsequently widely used and developed by later generations (Mnih and Hinton, 2008;Mikolov et al).…”

Section: Huffman Treementioning

confidence: 99%

HFMRE: Constructing Huffman Tree in Bags to Find Excellent Instances for Distantly Supervised Relation Extraction

Li,

Shao,

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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A fast and improved Image Compression technique using Huffman coding

Cited by 26 publications

References 2 publications

Pyramid based Progressive Transmission System with Hybrid Compression Scheme for Medical Images

Pyramid based Progressive Transmission System with Hybrid Compression Scheme for Medical Images

Resource-Restricted Environments Based Memory-Efficient Compressed Convolutional Neural Network Model for Image-Level Object Classification

HFMRE: Constructing Huffman Tree in Bags to Find Excellent Instances for Distantly Supervised Relation Extraction

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