A Class of Low-Complexity DCT-Like Transforms for Image and Video Coding

Low complexity DCT engine for image and video compression

In this paper, we defined a low complexity 2D-DCT architecture. The latter will be able to transform spatial pixels to spectral pixels while taking into account the constraints of the considered compression standard. Indeed, this work is our first attempt to obtain one reconfigurable multistandard DCT. Due to our new matrix decomposition, we could define one common 2D-DCT architecture. The cons...

Multiplierless 16-point DCT approximation for low-complexity image and video coding

An orthogonal 16-point approximate discrete cosine transform (DCT) is introduced. The proposed transform requires neither multiplications nor bit-shifting operations. A fast algorithm based on matrix factorization is introduced, requiring only 44 additions—the lowest arithmetic cost in literature. To assess the introduced transform, computational complexity, similarity with the exact DCT, and c...

Low-Complexity Lossless Codes for Image and Video Coding

We describe design of lossless block codes for geometric, Laplacian, and similar distributions frequently arising in image and video coding. Proposed codes can be understood as a generalization of Golomb codes, allowing more precise adaptation to values of parameters of distributions, and resulting in lower redundancy. Design of universal block codes for a class of geometric distributions is al...

Distributed Video Coding (DVC): Motion estimation and DCT quantization in low complexity video compression

Reduced-Complexity Shape-Adaptive DCT for Region-based Image Coding

We propose a computationally-efficient variant of the shapeadaptive discrete cosine transform (SA-DCT) currently considered for MPEG-4. Although the SA-DCT complexity is acceptable for 8 8 blocks, it is very high when complete regions are processed at once. To reduce the SA-DCT complexity, we replace its 1-D DCT with a quasi-DCT algorithm and we assure that the quasi-DCT basis functions are ver...