• R.Balachander
• G.Sakthivel

The Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restore sharpened images that are accompanied with ringing effects. Inspired by the success of deep convolutional networks (DCN) on super-resolution, This work formulate a compact and efficient network for seamless attenuation of different compression artifacts. The Artifacts Reduction Convolutional Neural Network is the innovative method to reduce the artifacts appears during image compression. The parameters used to evaluate the results are PSNR, PSNR-B, SSIM. This ARCNN achieves 93% of structural similarity which is best than any other model.

Image compression, Joint picture expert group, Peak signal to noise ratio.

"REDUCTION OF COMPRESSION ARTIFACTS IN DIGITAL IMAGES USING ARTIFACTS REDUCTION CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.94-96, November-2018, Available :http://www.jetir.org/papers/JETIR1811615.pdf

"REDUCTION OF COMPRESSION ARTIFACTS IN DIGITAL IMAGES USING ARTIFACTS REDUCTION CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp94-96, November-2018, Available at : http://www.jetir.org/papers/JETIR1811615.pdf