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.
KEYWORDS: Computer programming, High dynamic range imaging, Video coding, Video, Video compression, Standards development, Signal processing, Classification systems
Versatile Video Coding (H.266/VVC) was standardized in July 2020, around seven years after its predecessor, High Efficiency Video Coding (H.265/HEVC). Typical for a successor standard, VVC aims to offer 50% bitrate savings at similar visual quality, which was confirmed in official verification tests. While HEVC provided large compression efficiency improvements over Advanced Video Coding (H.264/AVC), fast development of video technology ecosystem required more in terms of functionality. This resulted in various amendments being specified for HEVC including screen content, scalability and 3D-video extensions, which fragmented the HEVC market, rendering only the base specification being widely supported across a wide range of devices. To mitigate this, the VVC standard was from the start designed with versatile use cases in mind, and provides wide-spread support already in the first version. Shortly after the finalization of VVC, an open optimized encoder implementation VVenC was published, aiming to provide the potential of VVC at shorter runtime than the VVC reference software VTM. VVenC also supports additional features like multi-threading, rate control and subjective quality optimizations. While the software is optimized for random-access high-resolution video encoding, it can be configured to be used in alternative use cases. This paper discusses the performance of VVenC beyond its main use case, using different configurations and content types. Application specific performance is also discussed. It is shown that VVenC can mostly match VTM performance with less computation, and provides attractive additional faster working points with bitrate reduction tradeoffs.
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