Versatile Video Coding (VVC) is the most recent and efficient video-compression standard of ITU-T and ISO/IEC. It follows the principle of a hybrid, block-based video codec and offers a high flexibility to select a coded representation of a video. While encoders can exploit this flexibility for compression efficiency, designing algorithms for fast encoding becomes a challenging problem. This problem has recently been attacked with data-driven methods that train suitable neural networks to steer the encoder decisions. On the other hand, an optimized and fast VVC software implementation is provided by Fraunhofer’s Versatile Video Encoder VVenC. The goal of this paper is to investigate whether these two approaches can be combined. To this end, we exemplarily incorporate a recent CNN-based approach that showed its efficiency for intra-picture coding in the VVC reference software VTM to VVenC. The CNN estimates parameters that restrict the multi-type tree (MTT) partitioning modes that are tested in rate-distortion optimization. To train the CNN, the approach considers the Lagrangian rate-distortion-time cost caused by the parameters. For performance evaluation, we compare the five operational points reachable with the VVenC presets to operational points that we reach by using the CNN jointly with the presets. Results show that the combination of both approaches is efficient and that there is room for further improvements.
The Intra Subpartition (ISP) mode is one of the intra prediction tools incorporated to the new Versatile Video Coding (VVC) standard. ISP divides a luma intra-predicted block along one dimension into 2 or 4 smaller blocks, called subpartitions, that are predicted using the same intra mode. This paper describes the design of this tool and its encoder search implementation in the VVC Test Model 7.3 (VTM-7.3) software. The main challenge of the ISP encoder search is the fact that the mode pre-selection based on the sum of absolute transformed differences typically utilized for intra prediction tools is not feasible in the ISP case, given that it would require knowing beforehand the values of the reconstructed samples of the subpartitions. For this reason, VTM employs a different strategy aimed to overcome this issue. The experimental tool-off tests carried out for the All Intra configuration show a gain of 0.52% for the 22-37 Quantization Parameter (QP) range with an associated encoder runtime of 85%. The results are improved to a 1.06% gain and an 87% encoder runtime in the case of the 32-47 QP range. Analogously, for the tool-on case the results for the 22-37 QP range are a 1.17% gain and a 134% encoder runtime and this improves in the 32-47 QP range to a 1.56% gain and a 126% encoder runtime.
Today’s hybrid video coding systems typically perform an intra-picture prediction whereby blocks of samples are predicted from previously decoded samples of the same picture. For example, HEVC uses a set of angular prediction patterns to exploit directional sample correlations. In this paper, we propose new intra-picture prediction modes whose construction consists of two steps: First, a set of features is extracted from the decoded samples. Second, these features are used to select a predefined image pattern as the prediction signal. Since several intra prediction modes are proposed for each block-shape, a specific signalization scheme is also proposed. Our intra prediction modes lead to significant coding gains over state of the art video coding technologies.
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