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.
Hassanpour, H., & Asadi, S. (2016). Deblocking Joint Photographic Experts Group Compressed Images via Self-learning Sparse Representation. International Journal of Engineering, 29(12), 1684-1690.
MLA
Hamid Hassanpour; Sekineh Asadi. "Deblocking Joint Photographic Experts Group Compressed Images via Self-learning Sparse Representation". International Journal of Engineering, 29, 12, 2016, 1684-1690.
HARVARD
Hassanpour, H., Asadi, S. (2016). 'Deblocking Joint Photographic Experts Group Compressed Images via Self-learning Sparse Representation', International Journal of Engineering, 29(12), pp. 1684-1690.
VANCOUVER
Hassanpour, H., Asadi, S. Deblocking Joint Photographic Experts Group Compressed Images via Self-learning Sparse Representation. International Journal of Engineering, 2016; 29(12): 1684-1690.