Abstract
This paper proposes a new post-processing scheme for blocking artifact reduction in JPEG-compressed images through wavelet domain adaptive non-local means (NLM) filtering. It is assumed that the degradation due to discrete cosine transform (DCT) domain quantization noise is some additive noise. The good denoising property of undecimated wavelet transform (UDWT) is utilized here. It is shown that there exist a strong relationship between the JPEG image quality factor (Q) and the experimentally observed ‘optimal’ NLM filter parameters. Based on a subband adaptive threshold, the detail coefficients are first classified into significant and non significant coefficients. The coefficients below the threshold are insignificant coefficients that are treated with adaptive NLM filter using proposed parameter selection strategy. With this strategy, the wavelet coefficients corresponding to edge structures are well preserved and the noisy ones are reduced effectively. The coefficients above the threshold are significant coefficients which are treated with adaptive NLM filter using different parameter selection strategy that yields comparatively less smoothing and avoids oversmoothing of important details. The proposed deblocking algorithm achieves high competitive performance compared to that of existing well-known image deblocking techniques both objectively and subjectively.
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Nath, V.K., Bujar Baruah, S.M., Hazarika, D., Baruah, H.G. (2018). A Deblocking Algorithm for JPEG-Compressed Images Using Wavelet Domain Adaptive Non-Local Means Filtering. In: Mandal, J., Saha, G., Kandar, D., Maji, A. (eds) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol 24. Springer, Singapore. https://doi.org/10.1007/978-981-10-6890-4_30
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DOI: https://doi.org/10.1007/978-981-10-6890-4_30
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