An image reconstruction approach in discrete cosine transform domain

ABSTRAKPada studi ini, dilakukan penggabungan metode - metode untuk memperkuat dan meningkatkan sisi keamanan proses pertukaran informasi atau pesan digital. Metode yang digunakan diantaranya adalah metode kriptografi dan metode steganografi. Implementasi pada sistem yang dibangun dilakukan dengan menyandikan pesan pada penerapan metode steganografi citra dalam menyembunyikan pesan tersandi yang dihasilkan ke dalam sebuah citra warna (RGB) dalam domain Discrete Cosine Transform dengan teknik penyisipan Spread Spectrum. Hasil penelitian menunjukan bahwa kualitas dari stego image sangat mirip dengan cover citra yang digunakan, berdasarkan perolehan nilai performansi objektif PSNR diatas 30 db dan subjektif MOS di atas nilai 4.Kata kunci: Steganografi, Discrete Cosine Transform, Spread Spectrum, PSNR, SNR ABSTRACTIn this study, a combination of methods was used to strengthen and enhance the security side of the process of exchanging information or digital messages. The methods used include cryptographic methods and steganography methods. The implementation of the system built is done by encoding the message on the application of the image steganography method in hiding the encrypted message generated into a color image (RGB) in the Discrete Cosine Transform domain with the Spread Spectrum insertion technique. The results of the study show that the quality of the stego image is very similar to the cover image used, based on the acquisition of an objective performance value of PSNR above 30 db and subjective MOS above a value of 4.Keywords: Steganografi, Discrete Cosine Transform, Spread Spectrum, PSNR, SNR

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DCT based feature extraction and support vector machine classification for musical instruments tone recognition

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i5.3158 ◽

2021 ◽

Vol 10 (5) ◽

pp. 2796-2803

Author(s):

Linggo Sumarno ◽

Rifai Chai

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Discrete Cosine Transform ◽

Recognition System ◽

Musical Instruments ◽

Combination Method ◽

Support Vector ◽

Transform Domain ◽

Cosine Transform ◽

Tone Recognition

The conducted research proposes a feature extraction and classification combination method that is used in a tone recognition system for musical instruments. It is expected that by implementing this combination, the tone recognition system will require fewer feature extraction coefficients than those previously investigated. The proposed combination comprises of feature extraction using discrete cosine transform (DCT) and classification using support vector machine (SVM). Bellyra, clarinet, and pianica tones were used in the experiment, with each indicating a tone with one, several, or many major local peaks in the transform domain. Based on the results of the tests, the proposed combination is efficient enough to be used in a tone recognition system for musical instruments. This is indicated in recognizing a tone, it only needs at least eight feature extraction coefficients.

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