A gradient-based optimization approach for reduction of blocking artifacts in JPEG images
作者：
Highlights：
• The block-based processing in JPEG as well as the quantization of DCT coefficients cause blocking artifacts in compressed JPEG images which become more visible at low quantization levels.
• This paper presents a gradient-based optimization approach to achieve reduction of blocking artifacts.
• The approach involves deblocking along rows and columns (treated as 1-D signals) by an optimization formulation which involves either a fixed-weight or an adaptive-weight.
• The optimization formulation is solved analytically using the information provided by the approximated gradient of original 1-D signals.
• A blocking artifacts reduced image is reconstructed by aggregating 1-D signals.
• The performance of the developed method is assessed by examining both gray-level and color images and by computing the three measures of PSNR, GBIM, and SSIM.
摘要
•The block-based processing in JPEG as well as the quantization of DCT coefficients cause blocking artifacts in compressed JPEG images which become more visible at low quantization levels.•This paper presents a gradient-based optimization approach to achieve reduction of blocking artifacts.•The approach involves deblocking along rows and columns (treated as 1-D signals) by an optimization formulation which involves either a fixed-weight or an adaptive-weight.•The optimization formulation is solved analytically using the information provided by the approximated gradient of original 1-D signals.•A blocking artifacts reduced image is reconstructed by aggregating 1-D signals.•The performance of the developed method is assessed by examining both gray-level and color images and by computing the three measures of PSNR, GBIM, and SSIM.
论文关键词：Reduction of blocking artifacts in JPEG images,Gradient-based optimization for reduction of blocking artifacts in JPEG images
论文评审过程：Received 19 April 2014, Revised 18 September 2014, Accepted 19 September 2014, Available online 30 September 2014.
论文官网地址：https://doi.org/10.1016/j.image.2014.09.008
