Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization

Savic, Milan; Perić, Zoran; Simic, Nikola

doi:10.1016/j.eswa.2015.05.037

Cited by 12 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to high performance in achieving minimum code length, Huffman is the most commonly used technique. Another two techniques that usually used for encoding are RLE and Arithmetic Coding Savić, Perić & Simić (2015) in their research proposed dual mode quantization by using low and medium number of quantization levels and fixing the code word length by using pixel value prediction in the pre-processing stage. Prediction is done on blocks with m x m sizes.…”

Section: Quantization Algorithm For Image Compressionmentioning

confidence: 99%

Thresholding And Quantization Algorithms for Image Compression Techniques: A Review

Lashari¹,

Ibrahim²,

Taujuddin³

et al. 2018

APJITM

View full text Add to dashboard Cite

With increasing demand on digital images, there is a need to compress the image to entertain the limited bandwidth and storage capacity. Recently, there is a growing interest among researchers focusing on compression of various types of images and data. Amongst various compression algorithms, transform-based compression is one of the promising algorithms. Despite the technological advances in transmission and storage, the demands placed on the bandwidth of communication and storage capacities by far outstrips its availability. This paper presents a review of image compression principle, compression techniques and various thresholding algorithms (pre-processing algorithms) and quantization algorithm (post-processing algorithms). This paper intends to give an overview to the relevant parties to choose the suitable image compression algorithms to suit with the need.

show abstract

Section: Quantization Algorithm For Image Compressionmentioning

confidence: 99%

Thresholding And Quantization Algorithms for Image Compression Techniques: A Review

Lashari¹,

Ibrahim²,

Taujuddin³

et al. 2018

APJITM

View full text Add to dashboard Cite

show abstract

“…Generally, all compression algorithms may be classified in two groups -"lossless" compression algorithms if there is no loss of information, and "lossy" methods if some information is lost irreversibly [1], [2]. Even though there is a variety of compression algorithms for different purposes [3], research areas are still expanding. In recent years, schemes which incorporate compressive sensing became very important and some restoration as well as image reconstruction schemes made an impact in the image processing field [4]- [5].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of non-uniform quantizers for discrete input samples and its application to an image processing algorithm

2017

View full text Add to dashboard Cite

This paper describes an algorithm for grayscale image compression based on non-uniform quantizers designed for discrete input samples. Non-uniform quantization is performed in two steps for unit variance, whereas design is done by introducing a discrete variance. The best theoretical and experimental results are obtained for those discrete values of variance which provide the operating range of quantizer located in the vicinity of maximal signal value that can appear on the entrance. The experiment is performed by applying proposed quantizers for compression of standard test grayscale images as a classic example of discrete input source. The proposed fixed non-uniform quantizers, designed for discrete input samples, provide up to 4.93 [dB] higher PSQNR compared to the fixed piecewise uniform quantizers designed for discrete input samples. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. III44006 and Grant no. TR32035]

show abstract

Color Image Compression Based on Feature Extraction

Pabi

Aruna

Puviarasan

2017

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization

Cited by 12 publications

References 17 publications

Thresholding And Quantization Algorithms for Image Compression Techniques: A Review

Thresholding And Quantization Algorithms for Image Compression Techniques: A Review

Design and implementation of non-uniform quantizers for discrete input samples and its application to an image processing algorithm

Color Image Compression Based on Feature Extraction

Contact Info

Product

Resources

About