Low-Complexity Loeffler DCT Approximations for Image and Video Coding
This work addresses the need for low-complexity DCT approximations in image and video coding systems, offering incremental improvements for encoders like JPEG, H.264/AVC, and H.265/HEVC.
The paper tackled the problem of high computational complexity in image and video coding by proposing a new class of eight-point DCT approximations based on a matrix parametrization method, resulting in Pareto-efficient approximations that were embedded into standard codecs and implemented on an FPGA, showing improved efficiency in area, speed, and power consumption compared to unmodified standards.
This paper introduced a matrix parametrization method based on the Loeffler discrete cosine transform (DCT) algorithm. As a result, a new class of eight-point DCT approximations was proposed, capable of unifying the mathematical formalism of several eight-point DCT approximations archived in the literature. Pareto-efficient DCT approximations are obtained through multicriteria optimization, where computational complexity, proximity, and coding performance are considered. Efficient approximations and their scaled 16- and 32-point versions are embedded into image and video encoders, including a JPEG-like codec and H.264/AVC and H.265/HEVC standards. Results are compared to the unmodified standard codecs. Efficient approximations are mapped and implemented on a Xilinx VLX240T FPGA and evaluated for area, speed, and power consumption.