An efficient image compression using pixel filter for social media applications. (7th March 2022)

• Record Type: Journal Article
• Authors: Makala, Ramesh; Ponnaboyina, Ranganath; Mamidisetti, Gowtham
• Is Part Of: International journal of innovative computing and applications. Volume 13:Number 1(2022)
• Page Start: 27
• Page End: 33
• Publication Date: 2022-03-07
• Subjects: colour map -- image compression -- filters -- quality enhancement; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Image data transfer has increased rigorously in the present times in social networking sites, mobile apps and live streaming video applications. This phenomenon puts enormous effect on internet bandwidth and speed of image transfer and loading. Image compression deals with this issue by reducing image sizes while maintaining quality aspects. Some of the areas that have involved image data usage include security surveillance, medical imaging, remote analysis and diagnosis, advertising, communication, and social media. Whereas an approach such as Bayer CFA image is popular and proves to be low-cost, alongside other conventional techniques that have been documented in the literature, however, an efficient image compression model that shows good performance with low cost, low power, and limited bandwidth is yet to be established, especially in relation to social media applications. We introduce a new filter which is applied on each pixel and compresses it. The proposed method classifies pixels into different buckets based on filter. Inverse process tries to restore pixel values back from buckets. With experimental results, we show compression and quality aspects variations based on filter selection.