Discrete cosine transform optimization in image compression based on genetic algorithm

Liu, Yuan Yuan; Chen, Hexin; Zhao, Yan; Sun, Hongyan

doi:10.1109/cisp.2015.7407875

Cited by 8 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… holds for any coding f [21]. By Shannon's theorem, the best compression when represented in binary form can be obtained by encoding characters with a relative frequency ) (s p using the -) ( log 2 s p bit [22]. The redundancy of Huffman coding approaches optimal only when the relative frequencies are multiples of two.…”

Section: ) ( S F Cmentioning

confidence: 99%

Method for Processing and Assessing the Degree of Digital Image Compression Based on Haar Transformation

Vychuzhanin¹,

Rudnichenko²,

Bercov³

et al. 2022

Computer Modeling and Intelligent Systems

View full text Add to dashboard Cite

Section: ) ( S F Cmentioning

confidence: 99%

Method for Processing and Assessing the Degree of Digital Image Compression Based on Haar Transformation

Vychuzhanin¹,

Rudnichenko²,

Bercov³

et al. 2022

Computer Modeling and Intelligent Systems

View full text Add to dashboard Cite

“…DCT divides images into several parts with different quality levels [21]. It is applied in various fields such as error control [22], improve image quality [23] and increase the efficiency of video coding [24,13].…”

Section: Features Extraction Using Dctmentioning

confidence: 99%

Combination of fast hybrid classification and k value optimization in k-nn for video face recognition

Septiana

Suciati

2020

register. jurnal. ilm. teknologi. sistem. inf.

View full text Add to dashboard Cite

Nowadays, the need for face recognition is no longer include images only but also videos. However, there are some challenges associated with the addition of this new technique such as how to determine the right pre-processing, feature extraction, and classification methods to obtain excellent performance. Although nowadays the k-Nearest Neighbor (k-NN) is widely used, high computational costs due to numerous features of the dataset and large amount of training data makes adequate processing difficult. Several studies have been conducted to improve the performance of k-NN using the FHC (Fast Hybrid Classification) method by optimizing the local k values. One of the disadvantages of the FHC Method is that the k value used is still in the default form. Therefore, this research proposes the use of k-NN value optimization methods in FHC, thereby, increasing its accuracy. The Fast Hybrid Classification which combines the k-means clustering with k-NN, groups the training data into several prototypes called TLDS (Two Level Data Structure). Furthermore, two classification levels are applied to label test data, with the first used to determine the n number of prototypes with the same class in the test data. The second classification using the optimized k value in the k-NN method, is employed to sharpen the accuracy, when the same number of prototypes does not reach n. The evaluation results show that this method provides 86% accuracy and time performance of 3.3 seconds.

show abstract

2D- Discrete Cosine Transform based Dynamically Controllable Image Compression Technique

Kumar

2020

2020 IEEE 22nd Electronics Packaging Technology Conference (EPTC)

View full text Add to dashboard Cite

Discrete cosine transform optimization in image compression based on genetic algorithm

Cited by 8 publications

References 6 publications

Method for Processing and Assessing the Degree of Digital Image Compression Based on Haar Transformation

Method for Processing and Assessing the Degree of Digital Image Compression Based on Haar Transformation

Combination of fast hybrid classification and k value optimization in k-nn for video face recognition

2D- Discrete Cosine Transform based Dynamically Controllable Image Compression Technique

Contact Info

Product

Resources

About