Image compression performance comparison of RLE and LZV algorithms for effective big data management: A case study

Şükrü Mustafa Kaya; Murat Aksel Akçay

Makale detayları

Image compression performance comparison of RLE and LZV algorithms for effective big data management: A case study

Yazarlar:

Şükrü Mustafa Kaya | Murat Aksel Akçay

Yayıncı:

Istanbul Aydin University

Ana Başlıklar:

Mühendislik

Dergi:

Anadolu Bil Meslek Yüksekokulu Dergisi Vol.17 Issue 66, pp.149-160

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001288077

pISSN:

1306-3375

Anahtar kelimeler:

Özet:

 Due to the rapid increase in digitization, the need for data storage in digital systems and network systems is increasing. As important as fixtures for public institutions and the private sector, It is vital that digital data is stored and backed up in a healthy and secure manner. At the same time, storing data on social media and similar digital platforms is becoming a necessity. As the need for data storage becomes a problem, different solutions are sought and smaller data storage possibilities are preferred. Data compression algorithms also serve this purpose in a sense. The main purpose of the studies in this field is to obtain less voluminous data by using compression methods from the raw data produced, to narrow the area covered by the data without destroying its original state, and to create data sets with low storage costs. There are many studies in the literature for this purpose. In this study, researches on the subject are carried out and different compression algorithms such as LZW, RLE are discussed. In addition, image files in different formats are compared based on their size, and image compression performances are tested based on the space occupied by image compression algorithms in memory.

 Due to the rapid increase in digitization, the need for data storage in digital systems and network systems is increasing. As important as fixtures for public institutions and the private sector, It is vital that digital data is stored and backed up in a healthy and secure manner. At the same time, storing data on social media and similar digital platforms is becoming a necessity. As the need for data storage becomes a problem, different solutions are sought and smaller data storage possibilities are preferred. Data compression algorithms also serve this purpose in a sense. The main purpose of the studies in this field is to obtain less voluminous data by using compression methods from the raw data produced, to narrow the area covered by the data without destroying its original state, and to create data sets with low storage costs. There are many studies in the literature for this purpose. In this study, researches on the subject are carried out and different compression algorithms such as LZW, RLE are discussed. In addition, image files in different formats are compared based on their size, and image compression performances are tested based on the space occupied by image compression algorithms in memory.

 Due to the rapid increase in digitization, the need for data storage in digital systems and network systems is increasing. As important as fixtures for public institutions and the private sector, It is vital that digital data is stored and backed up in a healthy and secure manner. At the same time, storing data on social media and similar digital platforms is becoming a necessity. As the need for data storage becomes a problem, different solutions are sought and smaller data storage possibilities are preferred. Data compression algorithms also serve this purpose in a sense. The main purpose of the studies in this field is to obtain less voluminous data by using compression methods from the raw data produced, to narrow the area covered by the data without destroying its original state, and to create data sets with low storage costs. There are many studies in the literature for this purpose. In this study, researches on the subject are carried out and different compression algorithms such as LZW, RLE are discussed. In addition, image files in different formats are compared based on their size, and image compression performances are tested based on the space occupied by image compression algorithms in memory.

 Dijitalleşmenin hızla artıyor olması dijital sistemlerdeki belleklerde ve network sistemlerde karşılaşılan veri saklama ihtiyacını artırmaktadır. Kamu kurumları ve özel sektör için demirbaşlar kadar önemli olan dijital verilerin sağlıklı ve güvenli şekilde saklanması ve yedeklenmesi hayati önem arz etmektedir. Aynı zamanda sosyal medya ve benzer dijital platformlarda veri saklama zorunlu bir ihtiyaca dönüşmektedir. Veri depolama ihtiyacı sorun haline geldikçe farklı çözüm yolları aranmakta ve daha küçük boyutlu veri saklama olanakları tercih edilmektedir. Veri sıkıştırma algoritmalarıda bir anlamda bu amaca hizmet etmektedir. Üretilen ham veriden sıkıştırma yöntemlerinden yararlanılarak daha az hacimli veri elde etmek, verinin orijinal halini bozmadan kapladığı alanı daraltmak ve saklama maliyetleri düşük veri setleri oluşturmak bu alanda yapılan çalışmaların temel amacı durumundadır. Literatürde bu amaca yönelik çok sayıda çalışma bulunmaktadır. Bu çalışmada da konuyla ilgili araştırmalar yapılmakta LZW,RLE gibi farklı sıkıştırma algoritmaları tartışılmaktadır. Ayrıca farklı formatlardaki görüntü dosyaları boyutları temel alınarak karşılaştırılmakta ve görüntü sıkıştırma algoritmalarının bellekte kapladığı alan baz alınarak görüntü sıkıştırma performansları test edilmektedir.

Makale detayları

Image compression performance comparison of RLE and LZV algorithms for effective big data management: A case study

Yazarlar:

Şükrü Mustafa Kaya | Murat Aksel Akçay

Yayıncı:

Istanbul Aydin University

Ana Başlıklar:

Mühendislik

Dergi:

Anadolu Bil Meslek Yüksekokulu Dergisi Vol.17 Issue 66, pp.149-160

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001288077

pISSN:

1306-3375

Image Compression

Image Processing

Big Data Management

LZW

RLEImage Compression

Image Processing

Big Data Management

LZW

RLE

Sayı

Makale detayları

Yazarlar: Şükrü Mustafa Kaya | Murat Aksel Akçay

Yayıncı: Istanbul Aydin University

Ana Başlıklar: Mühendislik

Dergi: Anadolu Bil Meslek Yüksekokulu Dergisi Vol.17 Issue 66, pp.149-160

Belge Tipi: Makale

Yayın Tarihi: 4/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001288077

pISSN: 1306-3375

Sayı

Yazarlar:

Şükrü Mustafa Kaya | Murat Aksel Akçay

Yayıncı:

Istanbul Aydin University

Ana Başlıklar:

Mühendislik

Dergi:

Anadolu Bil Meslek Yüksekokulu Dergisi Vol.17 Issue 66, pp.149-160

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001288077

pISSN:

1306-3375