Improving HEVC Encoding of Rendered Video Data Using True Motion Information - Herglotz, Christian; Müller, David; Weinlich, Andreas; Bauer, Frank; Ortner, Michael; Stamminger, Marc; Kaup, André (20th IEEE International Symposium on Multimedia (ISM), 2018)
This paper shows that motion vectors representing the true motion of an object in a scene can be exploited to improve the encoding process of computer generated video sequences. Therefore, a set of sequences is presented for which the true motion vectors of the corresponding objects were generated on a per-pixel basis during the rendering process. In addition to conventional motion estimation methods, it is proposed to exploit the computer generated motion vectors to enhance the rate-distortion performance. To this end, a motion vector mapping method including disocclusion handling is presented. It is shown that mean rate savings of 3.78% can be achieved.<br
Keywords: HEVC; motion vectors; blender; renderer; encoding

Open source HEVC analyzer for rapid prototyping (HARP) - Springer, Dominic; Schnurrer, Wolfgang; Weinlich, Andreas; Heindel, Andreas; Seiler, Jürgen; Kaup, André (IEEE International Conference on Image Processing, 2014)
The design of new HEVC extensions comes with the need for careful analysis of internal HEVC codec decisions. Several bitstream analyzers have evolved for this purpose and provide a visualization of encoder decisions as seen from a decoder viewpoint. None of the existing solutions is able to provide actual insight into the encoder and its RDO decision process. With one exception, all solutions are closed source and make adaption of their code to specific implementation needs impossible. Overall, development with the HM code base remains a time-consuming task. Here, we present the HEVC Analyzer for Rapid Prototyping (HARP), which directly addresses the above issues and is freely available under www.lms.lnt.de/HAR

Probability Distribution Estimation for Autoregressive Pixel-Predictive Image Coding - Weinlich, Andreas; Amon, Peter; Hutter, Andreas; Kaup, André (IEEE Transactions on Image Processing, 2016)
Pixelwise linear prediction using backward-adaptive least-squares or weighted least-squares estimation of prediction coefficients is currently among the state-of-the-art methods for lossless image compression. While current research is focused on mean intensity prediction of the pixel to be transmitted, best compression requires occurrence probability estimates for all possible intensity values. Apart from common heuristic approaches, we show how prediction error variance estimates can be derived from the (weighted) least-squares training region and how a complete probability distribution can be built based on an autoregressive image model. The analysis of image stationarity properties further allows deriving a novel formula for weight computation in weighted least-squares proofing and generalizing ad hoc equations from the literature. For sparse intensity distributions in non-natural images, a modified image model is presented. Evaluations were done in the newly developed C++ framework volumetric, artificial, and natural image lossless coder (Vanilc), which can compress a wide range of images, including 16-bit medical 3D volumes or multichannel data. A comparison with several of the best available lossless image codecs proofs that the method can achieve very competitive compression ratios. In terms of reproducible research, the source code of Vanilc has been made publi
Keywords: Vanilc; weighted least-squares weighting function; parametric distribution model; variance estimation; 16 bit lossless medical 3D image compression; histogram sparsification