DCTlab: Educational Software for Still Image Compression and its Application in a Digital Television Course

2001 ◽  
Vol 38 (3) ◽  
pp. 187-198 ◽  
Author(s):  
Mislav Grgić ◽  
Sonja Grgić ◽  
Branka Zovko-Cihlar
Keyword(s):  
Image Compression ◽  
Learning Outcomes ◽  
Discrete Cosine Transform ◽  
Educational Software ◽  
Digital Television ◽  
Picture Quality ◽  
Still Image ◽  
Moving Images ◽  
Spatial Redundancy ◽  
Current Standards

Current standards for the compression of still and moving images use Discrete Cosine Transform (DCT) to remove spatial redundancy in images. Students specialising in image and video system engineering need to know why DCT is important in their field of interest and to understand the influence of DCT-based image compression on picture quality. Therefore, we have developed educational software, called DCTlab, that helps students to analyse DCT application in still image compression systems. This paper describes software characteristics, its application in a digital television course and learning outcomes.

scholarly journals A Novel Optimized Golomb-Rice Technique for the Reconstruction in Lossless Compression of Digital Images

2013 ◽  
Vol 2013 ◽  
pp. 1-5
Author(s):  
Shaik. Mahaboob Basha ◽  
B. C. Jinaga
Keyword(s):  
Image Compression ◽  
Digital Images ◽  
Visual Quality ◽  
Natural Images ◽  
Picture Quality ◽  
Compression Efficiency ◽  
Still Image ◽  
Efficiency Parameter ◽  
Image Compression Algorithm

The research trends that are available in the area of image compression for various imaging applications are not adequate for some of the applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm based on Golomb-Rice coding which was efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of peak signal-to-noise ratios (PSNR). We considered medical images, satellite extracted images, and natural images for the inspection and proposed a novel technique to increase the visual quality of the reconstructed image.

scholarly journals A PREDICTIVE CODING METHOD FOR LOSSLESS COMPRESSION OF IMAGES

2013 ◽  
Vol 7 (3) ◽  
pp. 683-685
Author(s):  
Anil Mishra ◽  
Ms. Savita Shiwani
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Video Processing ◽  
Prediction Method ◽  
Original Form ◽  
Minimum Entropy ◽  
Digital World ◽  
Adaptive Prediction ◽  
Continuous Tone ◽  
Spatial Redundancy

Images are an important part of today's digital world. However, due to the large quantity of data needed to represent modern imagery the storage of such data can be expensive. Thus, work on efficient image storage (image compression) has the potential to reduce storage costs and enable new applications.This lossless image compression has uses in medical, scientific and professional video processing applications.Compression is a process, in which given size of data is compressed to a smaller size. Storing and sending images to its original form can present a problem in terms of storage space and transmission speed.Compression is efficient for storing and transmission purpose.In this paper we described a new lossless adaptive prediction based algorithm for continuous tone images. In continuous tone images spatial redundancy exists.Our approach is to develop a new backward adaptive prediction techniques to reduce spatial redundancy in a image.The new prediction technique known as Modifed Gradient Adjusted Predictor (MGAP) is developed. MGAP is based on the prediction method used in Context based Adaptive Lossless Image Coding (CALIC). An adaptive selection method which selects the predictor in a slope bin in terms of minimum entropy improves the compression performance.

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