Date of Award
Santa Clara : Santa Clara University, 2023.
Doctor of Philosophy (PhD)
Computer Science and Engineering
In video compression, specifically in the prediction process, a residual signal is calculated by subtracting the predicted from the original signal, which represents the error of this process. This residual signal is usually transformed by a discrete cosine transform (DCT) from the pixel, into the frequency domain. It is then quantized, which filters more or less high frequencies (depending on a quality parameter). The quantized signal is then entropy encoded usually by a context-adaptive binary arithmetic coding engine (CABAC), and written into a bitstream. In the decoding phase the process is reversed. DCT and quantization in combination are efficient tools, but they are not performing well at lower bitrates and creates distortion and side effect. The proposed method uses sparse coding as an alternate transform which compresses well at lower bitrates, but not well at high bitrates. The decision which transform is used is based on a rate-distortion optimization (RDO) cost calculation to get both transforms in their optimal performance range. The proposed method is implemented in high efficient video coding (HEVC) test model HM-16.18 and high efficient video coding for screen content coding (HEVC-SCC) for test model HM-16.18+SCM-8.7, with a Bjontegaard rate difference (BD-rate) saving, which archives up to 5.5%, compared to the standard.
Schimpf, Michael G., "On Sparse Coding as an Alternate Transform in Video Coding" (2023). Engineering Ph.D. Theses. 48.