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Low-Complexity Loeffler DCT Approximations for Image and Video Coding

arXiv 2207.14463 ↗First posted 29 Jul 2022eess.IV cs.CV cs.MMcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 79 references that are in the map. First posted on arXiv 29 Jul 2022.

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7 of 79 references.

References · 7

  1. 2014A DCT Approximation for Image Compressioncited by 9
  2. 2017DCT-like Transform for Image Compression Requires 14 Additions Onlycited by 7
  3. 2013Low-complexity 8-point DCT Approximations Based on Integer Functionscited by 6
  4. 2015Improved 8-point Approximate DCT for Image and Video Compression Requiring Only 14 Additionscited by 6
  5. 2020An Integer Approximation Method for Discrete Sinusoidal Transformscited by 5
  6. 2015A Class of DCT Approximations Based on the Feig-Winograd Algorithmcited by 4
  7. 2016Low-complexity Pruned 8-point DCT Approximations for Image Encodingcited by 1

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