Fast and efficient lossless encoder in image compression with low computation and low memory

Karthikeyan, N.; Saravanakumar, N. M.; Sivakumar, M.

doi:10.1049/ipr2.12235

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…During the investigation, as there is no information or quality loss for lossless compression [62], images extracted from lossless compression versions (i.e. TIF, PNG, BMP) are used as references for evaluating image qualities after approximation considering formats and resolutions.…”

Section: Experimental Analysis For Hvs‐based Applicationsmentioning

confidence: 99%

A feasibility analysis of image approximation with image quality assessments

Rahman,

Huq,

Tunny

et al. 2023

IET Image Processing

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With technological developments, the resolution of the display systems is increasing, causing image data to increase. Several technologies are aiding in optimizing these data during transmission and storage while maintaining quality. Still, the internal data transmission for accessing the memory sub‐system during reading and writing cycles consumes significant power while requiring large memory for storage, which is inefficient, especially for portable displays. Hence, this work investigates the feasibility of reducing data through approximation for images in different formats and resolutions for human and computer vision based applications. Different image quality assessment metrics are utilized for performance evaluations with optimum image quality assessment selection according to application requirements. In addition, the feasibility analysis is conducted for three different applications as examples of computer vision. The experimental analysis highlights theoretical and industrial importance by showing that image data can be reduced from 25.0% to 62.5%, while satisfying requirements for HVS‐based and computer vision‐based applications.

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Section: Experimental Analysis For Hvs‐based Applicationsmentioning

confidence: 99%

A feasibility analysis of image approximation with image quality assessments

Rahman,

Huq,

Tunny

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Segmentation is an important step in image analysis, image understanding and video coding, and it is also a basic technology in computer vision. This is because image segmentation, object separation, feature extraction and parameter measurement transform the original image into a more abstract and compact form, which makes it possible for higher-level analysis and understanding [19].…”

Section: ) Digital Image Compression Technology: With the Continuous ...mentioning

confidence: 99%

Urban Image Segmentation in Media Integration Era Based on Improved Sparse Matrix Generation of Digital Image Processing

Zheng,

Xie

2023

IJACSA

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Media integration integrates the resources of various media platforms, including audience, technical and human resources. In the era of media integration, media in various channels, at different levels and in different fields provide various choices for image communication and brand building of cities. The technology of image processing by computer has gradually affected all aspects of people's life and work, bringing more and more convenience to people. In this paper, the application of digital image processing technology in city image communication in the age of media integration is studied. A new sparse matrix creation method is proposed, and the created sparse matrix is used as the similarity matrix to segment the spectral clustering image, so that the edge contour weakened in gradient calculation can be corrected and strengthened again. The research shows that the improved algorithm is superior to the traditional algorithm, and compared with the fuzzy entropy algorithm based on exhaustive search, the gray contrast between regions and Bezdek partition coefficient are improved by 9.301% and 4.127%. In terms of speed, the algorithm in this paper has absolute advantages, so our research is also affirmed, which fully shows that it should have high application value.

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“…Decreasing the variety between adjacent pixels yielded an increase in compression ratio, and vice versa. Karthikeyan et al (2021) proposed a frequency-based lossless new encoding technique that doesn't require any table similar to Huffman and Golomb Rice encoder and doesn't take high computation time like an arithmetic encoder. The proposed encoder was tested with nearly 200 standard images, and the results were compared with the standard encoders.…”

Section: Tellez Et Al (2020) Used a Neural Image Compression (Nic)mentioning

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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Fast and efficient lossless encoder in image compression with low computation and low memory

Cited by 4 publications

References 24 publications

A feasibility analysis of image approximation with image quality assessments

A feasibility analysis of image approximation with image quality assessments

Urban Image Segmentation in Media Integration Era Based on Improved Sparse Matrix Generation of Digital Image Processing

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

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