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Keywords: Biorthogonal transform Image compression JPEG algorithm All phase biorthogonal transform (APBT) Dual biorthogonal basis vectors Walsh transform (WT) Discrete cosine transform (DCT) Inverse discrete cosine transform (IDCT) Peak signal to noise ratio (PSNR) a b s t r a c t This paper proposes new concepts of the all phase biorthogonal transform(More)
—In this paper shape adaptive all phase biorthogonal transform (SA-APBT) for coding arbitrarily shaped image segments is proposed. The proposed transform can be used in region-based image compression instead of the shape adaptive discrete cosine transform (SA-DCT). Region-based image coding method is made up of three procedures: image segmentation, contour(More)
—In video compression, it can reduce the redundant information efficiently by motion estimation (ME) and motion compensation (MC), and less code is used to encode as much information as possible. In this paper, I frame encoding adopts wavelet transform and set partitioning in hierarchical trees (SPIHT) algorithm; for P frames, each frame sets the(More)
We introduce a new 2D graphic representation of DNA sequences considering codon degeneracy. Derived from the graphic representation, a multi-component vector is proposed to characterize quantitatively DNA sequences. Then we use the graphic representation and the vector to perform the phylogenetic analysis on two datasets, the complete coding sequences of(More)
 Abstract—Based on the block compressed sensing (BCS) framework, a new and non-orthogonal transform named all phase biorthogonal transform (APBT) is introduced to exploit the image sparsity, reduce the encoding complexity and be applicable to the blocked image easily. APBT exploits the signal sparsity better than DCT, and meanwhile it overcomes the defects(More)
—Recent efforts have shown that the reconstruction performance could be improved with optimized sensing matrix according to a given dictionary for a compressed sensing (CS) system. The existed optimizing conditions are mainly used to address the worst-case performance of CS recovery. Considering the quality of a sensing matrix with respect to the mean(More)