Evaluation of lossless compression techniques

Rao, S. Srinivasa; Bhat, Pratima

doi:10.1109/iccsp.2015.7322799

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…[A]Smitha Rao et al [4] Many lossy compression techniques have been addressed in this research, and many challenges for different types of compression in many applications have been discussed. The statistical method has been adopted in text compression, and a comparison has been made between LZ77 and LZ78, and this algorithm has provided the best result compared to the Huffman and the RUN algorithm from statistical methods.…”

Section: Related Workmentioning

confidence: 99%

New Technology to improve an Image Compression using the LZW Algorithm

2023

IJSER

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Image compression is a process applied to reduce the size of an image file, byte by byte, without reducing its quality acceptability levels. By reducing the file size, more images can be stored dona given amount of disk or memory space. Thus, bandwidth is required when images are transmitted over the Internet or downloaded from a web page, reducing network congestion and speeding contented delivery . The proposed system adopted the LZW algorithm to compress images without loss. This algorithm was named after the names of the scientists who developed this algorithm in recognition of their efforts, namely Abraham Lempel ,Jakob Ziv and Terry Welch, which is based on a dynamic dictionary, where this dictionary checks the file for duplicate data and then stores it in the dictionary, where this algorithm replaces strings of characters with individual symbols, as this proposed system performs 12-bit codes, and it was applied to many images and we got impressive results. The importance of this system lies in the benefit it provides by reducing the size of the image file without losing data, and thus reducing the space needed to store and send it easily over the Internet and high quality. The Python language has been used to implement this system.

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Section: Related Workmentioning

confidence: 99%

New Technology to improve an Image Compression using the LZW Algorithm

2023

IJSER

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“…Huffman ile görüntü sıkıştırma alanında daha hızlı çalışan bir algoritma Hashing yöntemi kullanılarak Reddy ve arkadaşları tarafından önerilmiştir [3]. 2015 yılında yapılan bir çalışmada ise kayıpsız veri sıkıştırma yöntemleri listelenerek algoritmik karmaşıklıkları, hesaplama süreleri, uygulanabilir alanları gibi açılar ele alınarak incelenmiştir [4]. Ayrıca farklı yöntemlerle performans artırımı üzerine önerilen bazı çalışmalar vardır [5], [6].…”

Section: şEkil 1 Kayıpsız Ve Kayıplı Sıkıştırmaunclassified

Huffman Algoritmasıyla Kayıpsız Hızlı Metin Sıkıştırma

Bulut¹

2016

El-Cezeri Fen Ve Mühendislik Dergisi

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Huffman Algoritması entropi kodlama yöntemiyle yapılan kayıpsız bir veri sıkıştırma yöntemidir. Bu yöntemde her karakter için ikilik sayı sistemde özel bir kod üretir. Veri içerisinde en çok kullanılan karakter için en kısa, en az kullanılan karakter için ise en uzun kod üretir. Huffman yönteminin uygulamasında kullanılan klasik yöntem ikili ağaç veri yapısıdır. Bu çalışmada Huffman yöntemi, önerilen bir yöntemle kodlanarak hesaplama süresi klasik yönteme göre azaltılmıştır. Bir matris tablo yardımıyla gerçekleştirilen kodlama uygulamalarında metin dosyaları bilinen Huffman kodlama yöntemine göre daha hızlı bir şekilde sıkıştırılabilmiştir.

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“…All pairs on B region are the changeable pairs. The pairs cannot be used for embedding watermark, but the pairs must be transformed using (1). And the non-embeddable pairs are on C region.…”

Section: Reversible Low Contrast Mappingmentioning

confidence: 99%

“…The development of compression method is based on the fact that there are a lot of redundancies on data. The redundancy can be: Spatial redundancy, correlation between neighboring pixel values in case of grey scale image; Spectral redundancy, correlation of color planes or spectral bands in a color image; Temporal redundancy, correlation between image sequences in adjacent video frames; Coding redundancy, utilization the probability distribution associated with the occurrence of the symbols; and Psycho visual redundancy, human vision does not respond with equal sensitivity to all visual information [1]. These differences cause different approaches in the compression method.…”

Section: Introductionmentioning

confidence: 99%

New Lossless Compression Method using Cyclic Reversible Low Contrast Mapping (CRLCM)

Mesra

Tjandrasa

Fatichah

2016

IJECE

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<p>In general, the compression method is developed to reduce the redundancy of data. This study uses a different approach to embed some bits of datum in image data into other datum using a Reversible Low Contrast Mapping (RLCM) transformation. Besides using the RLCM for embedding, this method also applies the properties of RLCM to compress the datum before it is embedded. In its algorithm, the proposed method engages Queue and Recursive Indexing. The algorithm encodes the data in a cyclic manner. In contrast to RLCM, the proposed method is a coding method as Huffman coding. This research uses publicly available image data to examine the proposed method. For all testing images, the proposed method has higher compression ratio than the Huffman coding.</p>

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Evaluation of lossless compression techniques

Cited by 6 publications

References 12 publications

New Technology to improve an Image Compression using the LZW Algorithm

New Technology to improve an Image Compression using the LZW Algorithm

Huffman Algoritmasıyla Kayıpsız Hızlı Metin Sıkıştırma

New Lossless Compression Method using Cyclic Reversible Low Contrast Mapping (CRLCM)

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