An Extensive Survey on Compression Algorithm for Effective Image Compression

Asthana, Stuti; Goyal, Dinesh; Pandit, Amitkant; Bhujade, Rakesh Kumar

doi:10.2139/ssrn.3170761

Cited by 1 publication

(1 citation statement)

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“…The importance of compression algorithms on the compression rates and processing times presents a significant challenge, especially with the increase in day-to-day big data concepts [1]. The compression process role represents an image using a few bits, which increases the compression ratio while keeping an acceptable quality of the image [2]. Unless the JPEG standards that use the discrete cosine transform and the JPEG2000 operate with the wavelet transform, most techniques proposed in the literature attempt to present and use other combinations of algorithms to optimize and minimize compression-induced degradation by having high compression ratios.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation

Bouida¹,

Khelifi²,

Beladgham³

et al. 2021

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In image processing, using compression is very important in various applications, especially those using data quantities in transmission and storing. This importance becomes most required with the evolution of image quantities and the big data systems explosion. The image compression allows reducing the required binary volume of image data by encoding the image for transmission goal or database saving. The principal problem with image compression when reducing its size is the degradation that enters the image. This degradation can affect the quality of use of the compressed image. To evaluate and qualify this quality, we investigate the use of textural combined image quality metrics (TCQ) based on the fusion of full reference structural, textural, and edge evaluation metrics. To optimize this metric, we use the Monte Carlo optimization method. This approach allows us to qualify our compressed images and propose the best metric that evaluates compressed images according to several textural quality aspects.

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Section: Introductionmentioning

confidence: 99%