An Efficient Image Compression Algorithm Based on Histogram Based Block Optimization and Arithmetic Coding

Dutta, Subarna; Abhinav, Aditya; Dutta, Partha; Kumar, Purushottam; Halder, Amiya

doi:10.7763/ijcte.2012.v4.614

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…However, some image blurring was evident, particularly at sulcal boundaries and at gray matter-white matter interfaces. Dutta et al (2012) proposed a two-step process for image compression. In the first step, the input image was divided into blocks of sub-images of various sizes (2x2, 3x3, 4x4, etc.…”

Section: Related Work On Image Compressionmentioning

confidence: 99%

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

2023

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Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase.

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Section: Related Work On Image Compressionmentioning

confidence: 99%

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

2023

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“…It may be in the form of picture, audio, video or a text file. In [9] better compression ratio and comparable PSNR value has been achieved as compared to traditional JPEG by using histogram-based block optimization & arithmetic coding technique is used for compressing and decompressing a lossy image. But the algorithm was only implemented for 8-bit grey scale image.…”

Section: Related Workmentioning

confidence: 99%

The Improvised GZIP, A Technique for Real Time Lossless Data Compression

Shah¹,

Sethi²

2019

EAI Endorsed Transactions on Context-aware Systems and Applicat

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Whenever it comes to data processing, the user always faces two major constraints. One is storage capacity and second is bandwidth. These two resources must be efficiently utilized by compressing the data. Enormous algorithms are used to compress data. As far as, compression in storage is concern, GZIP is used on large scale for lossless data compression. However, it is not desirable to carry out lossless data compression for real time data. In this paper, an improvisation is proposed in the existing GZIP algorithm for compressing real time data by a contemporary concept of introducing Adaptive Huffman algorithm by replacing the traditional Huffman encoder (static). Simulations have proved that improvised GZIP has approximate 18% better compression ratio and space saving than traditional GZIP for real time data. This research paper extends the usability of GZIP algorithm to carry out lossless compression for real time data.

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“…Compressing data for managing it in huge amounts can be often accomplished [1] most conveniently by histograms [2]. As is well known, for example, histograms keep track of the frequency of events [3] by storing the information in a user-defined memory which, in first approximation, can be dimensioned independently of the number of events.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Computing of Real-Time and Multichannel Histograms: A Full FPGA Approach

et al. 2022

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In a world heading towards applications, in science and industry, based on big data processing, the ability to elaborate streams of data is becoming more important every day. Applications of various natures, ranging from biology to chemistry, from medical imaging to spectroscopy, need systems able to detect, process and store huge amounts of data in real-time. In this context, techniques such as histogramming come into play. Histograms are able to represent data shapes and retrieve statistical information, favoring further processing. This kind of processing is usually done with the help of general purpose processors, relying on temporal computing, with their pros (simplicity and fast operating frequencies) and cons (inability to exploit parallel computation). Both Industry and Academia have, however, proposed many solutions to this need, delivering histogram generators both in full-custom Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Array (FPGA) IP-Cores, preferred over processors thanks to their superior parallel computing power. In this work, we present a full FPGA approach to this issue, resulting in a high-performance IP-Core for generating and managing multiple histograms in real time, each supporting a data flow up to 224 MSps, with a strong focus on overall flexibility and area efficiency, using as low as < 250 LUTs and 300 FFs resources for a 16 bit-wide, 4096-bin histogram implementation. The IP-Core has been successfully integrated and validated in a high-performance time-mode application: an FPGA-based Time-to-Digital Converter. The resulting IP-Core is, more in general, a single solution perfectly adaptable to any field of application and FPGA device.INDEX TERMS Field-programmable gate array (FPGA), histograms, real-time systems, IP-Cores, throughput.

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An Efficient Image Compression Algorithm Based on Histogram Based Block Optimization and Arithmetic Coding

Cited by 6 publications

References 10 publications

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

The Improvised GZIP, A Technique for Real Time Lossless Data Compression

High-Performance Computing of Real-Time and Multichannel Histograms: A Full FPGA Approach

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