|Title: ||Compression artifacts reduction with multiscale tensor regularization
||Author(s): ||Surya Prasath, V.B.
||Keywords: ||Artifact reduction; Edge preserving; JPEG deblocking; Multiscale; Regularization; Structure tensor; Constrained optimization; Discrete cosine transforms; Eigenvalues and eigenfunctions; Image coding; Metadata; Artifact removal; Compression artifacts; Constrained minimization; Data fidelity; Discrete cosine transformation; Natural image database; Structure tensors; Variable exponents; Tensors
||Abstract: ||We study a multiscale tensor regularization based JPEG decompression artifact removal in digital images. Structure tensor eigenvalues based robust edge map is used within a variable exponent regularization. Variational constrained minimization which combines data fidelity driven by color subsampling and discrete cosine transformation operator is utilized. Experimental results across different compression levels and with various error metrics indicate our proposed method obtains high quality results on cartoon/clip-art and LIVE1 natural image databases.
||Issue Date: ||2021
||Series/Report no.: ||Vol. 32
|Appears in Collections:||INTERNATIONAL PUBLICATIONS|