Secured Color Image Compression based on Compressive Sampling and Lü System

Sundarakrishnan, Sundarakrishnan; Jaison, B; Raja, S. P.

doi:10.5755/j01.itc.49.3.25901

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…The content adaptive optimization strategy also generated better latent representation without architecture refinements. Krishnan et al (2020) proposed an image compression method based on compressive sampling and the Lü system. Specifically, the input image was sparsely represented on a transform basis, and compressive sampling measurements were obtained from these sparse transform coefficients using an incoherent sensing matrix.…”

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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“…The DCC mark is utilized to prune away unessential pictures to a given query before assessing more costly comparability measures on the planned candidate data. References [4,5] proposed a semantically enhanced information retrieval model for fine gaining the indexing and querying scope. Irrelevant words were also filtered out from the key.…”

Section: Related Workmentioning

confidence: 99%

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Karthikeyan¹,

Kavida²,

Suresh³

2022

Computer Systems Science and Engineering

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Digitization has created an abundance of new information sources by altering how pictures are captured. Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing. This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets. Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional, and c) noisy and less accurate extraction of image information (semantic and predicted attributes). This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively. Thus, retrieval becomes straightforward and rapid. This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost. We focus on the schema design on a non-clustered index solution, especially cover queries. This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing. Finally, we include non-key columns in addition to the key columns. Experiments on two image data sets 'with and without' filtered indexing show low query cost. We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing. The results show that retrieval by using deep root indexing is simple and fast.

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