Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 29, No. 12 (December 2016) 1684-1690    Article in Press

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  DEBLOCKING JOINT PHOTOGRAPHIC EXPERTS GROUP COMPRESSED IMAGES VIA SELF-LEARNING SPARSE REPRESENTATION
 
S. Asadi Amiri and H. Hassanpour
 
( Received: July 19, 2016 – Accepted in Revised Form: November 11, 2016 )
 
 

Abstract    JPEG is one of the most widely used image compression method, but it causes annoying blocking artifacts at low bit-rates. Sparse representation is an efficient technique which can solve many inverse problems in image processing applications such as denoising and deblocking. In this paper, a post-processing method is proposed for reducing JPEG blocking effects via sparse representation. In this method, a dictionary is learned via the single input blocky image using K-SVD. There is no need for any prior knowledge about the blocking artifacts. Experimental results on various images demonstrate that the proposed post-processing method can efficiently alleviate the blocking effects at low bit-rates and outperforms the existing methods.

 

Keywords    Image compression, blocking effect, post-processing, sparse representation, low bit-rate

 

چکیده    یکی از پر کاربردترین روش فشرده­سازی تصویر است، اما این روش منجر به اثرات بلوکی آزاردهنده در در نرخ­های بیت پایین می­شود. نمایش تنک یک تکنیک کارآمد است که می­تواند بسیاری از مسائل معکوس را در کاربردهای پردازش تصویر همچون حذف نویز و حذف اثر بلوکی حل نماید. در این مقاله، یک روش پس‌پردازش برای کاهش اثرات بلوکی با نمایش تنک پیشنهاد شده است. در این روش، یک واژه­نامه با تک تصویر بلوکی ورودی با استفاده از K-SVD آموزش داده می‌شود. نیازی به دانستن دانش پیشین در مورد اثرات بلوکی نمی­باشد. نتایج تجربی بر روی تصاویر مختلف نشان داد که پس­پردازش پیشنهای به صورت کارآمد می­تواند اثرات بلوکی را در نرخ بیت پایین حذف نماید و برتر از روش­های موجود است.

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