DCT Approximations Based on Chen's Factorization
This work addresses computational efficiency in image and video compression for applications like JPEG and HEVC, but it is incremental as it builds on existing factorization methods.
The paper tackled the problem of high computational cost in DCT-based image and video compression by proposing two multiplication-free 8-point DCT approximations based on Chen's factorization, which were scaled to 16- and 32-point versions and integrated into HEVC, showing they outperform traditional and state-of-the-art methods with very low complexity.
In this paper, two 8-point multiplication-free DCT approximations based on the Chen's factorization are proposed and their fast algorithms are also derived. Both transformations are assessed in terms of computational cost, error energy, and coding gain. Experiments with a JPEG-like image compression scheme are performed and results are compared with competing methods. The proposed low-complexity transforms are scaled according to Jridi-Alfalou-Meher algorithm to effect 16- and 32-point approximations. The new sets of transformations are embedded into an HEVC reference software to provide a fully HEVC-compliant video coding scheme. We show that approximate transforms can outperform traditional transforms and state-of-the-art methods at a very low complexity cost.