This work investigates video encoding parallelization techniques based on the VVC standard, using the open and optimized encoder software implementation VVenC. Modern multi-processor systems offer significant opportunities for accelerating video encoding. By employing a proposed combination of parallelization methods, the VVenC encoder achieves an acceleration factor of up to 22 compared to single-threaded mode on a 32-core system, with potential increases to 27x at higher bitrates. Building upon prior work on inter-frame parallelization (IFP), the study introduces frame region-based synchronization, enabling further acceleration of up to 10%. Beyond that, the study demonstrates extending frame parallelization beyond Group of Pictures (GOP) boundaries, which improves IFP speed up by 37% and 11% at HD and UHD resolutions, respectively. Additional combinations with other VVC parallelization tools, such as tiles and VVC Wavefront Parallel Processing (WPP), are also explored. The paper provides a comprehensive analysis of parallelization challenges and highlights areas for further improvement.

Adaptive video streaming has facilitated improved video streaming over the past years. A balance among coding performance objectives such as bitrate, video quality, and decoding complexity is required to achieve efficient, content- and codec-dependent, adaptive video streaming. This paper proposes a multi-objective Pareto-front (PF) optimization framework to construct quality-monotonic, content-adaptive bitrate ladders Versatile Video Coding (VVC) streaming that jointly optimize video quality, bitrate, and decoding time, which is used as a practical proxy for decoding energy. Two strategies are introduced: the Joint Rate-Quality-Time Pareto Front (JRQT-PF) and the Joint Quality-Time Pareto Front (JQT-PF), each exploring different tradeoff formulations and objective prioritizations. The ladders are constructed under quality monotonicity constraints during adaptive streaming to ensure a consistent Quality of Experience (QoE). Experiments are conducted on a large-scale UHD dataset (Inter-4K), with quality assessed using PSNR, VMAF, and XPSNR, and complexity measured via decoding time and energy consumption. The JQT-PF method achieves 11.76

Versatile Video Coding (VVC) offers substantial improvements in compression efficiency compared to its predecessor, High Efficiency Video Coding (HEVC). However, these gains come at the cost of significantly increased encoding complexity, particularly due to VVC’s quadtree with nested multi-type tree (QTMT) partitioning structure. This complexity poses challenges for real-time and low-latency applications. This paper presents an enhanced split cost prediction framework designed to accelerate the VVC encoding process while preserving compression efficiency. Building on prior split cost prediction methods, our approach refines context modeling by incorporating Coding Unit (CU) size, prediction mode, and partitioning depth features. We construct a large-scale training dataset and optimize context-dependent predictors for multiple encoder presets. The proposed method is implemented in both Fraunhofer Versatile Video Encoder (VVenC) and the VVC Test Model (VTM), demonstrating encoding time reductions of up to 6% with negligible compression efficiency loss compared to the anchor using the benchmark method. Performance improvements are most significant in the medium and slower presets, where the partitioning search space is larger. Our results demonstrate that split cost prediction can be further optimized through feature-driven context modeling, eliminating the need for complex machine learning models, thereby making it practical for deployment in existing encoder pipelines.

The demand for efficient multi-rate encoding techniques has surged with the increasing prevalence of ultra-high-definition (UHD) video content, particularly in adaptive streaming scenarios where a single video must be encoded at multiple bitrates to accommodate diverse network conditions. While Versatile Video Coding (VVC) significantly improves compression efficiency, it introduces considerable computational complexity, making multi-rate encoding a resource-intensive task. This paper examines coding unit (CU) partitioning strategies to minimize redundant computations in VVC while preserving high video quality. We propose single- and double-bound approaches, leveraging CU depth constraints from reference encodes to guide dependent encodes across multiple QPs. These methods are evaluated using VVenC with various presets, demonstrating consistent improvements in encoding efficiency. Our methods achieve up to 11.69

An important new tool in Versatile Video Coding (VVC) is the extended block partitioning scheme. It enables recursive block subdivisions with five different split modes. This flexibility largely contributes to VVC's compression performance, but also increases the encoder complexity. There have been many methods proposed in the literature to speed up the partitioning search in VVC encoders, from simple heuristics to complex approaches based on neural networks. This paper intends to review this literature and compare it to a simple complexity reduction technique, namely progressive reduction of available split-depths. For each reviewed paper, a working point is found that roughly matches either the shown runtime reduction or bitrate increase. Our proposed method produces a variety of working points with its convex hull including the operating points of many approaches discussed in the literature. Only half of the reviewed works slightly outperform the envelope of proposed working points. This review and performance evaluation study intends to put into perspective known VVC encoder optimization research from literature when compared to our proposed common baseline method that may serve as a benchmark for future work in this area.

Traditional per-title encoding schemes aim to optimize encoding resolutions to deliver the highest perceptual quality for each representation. However, keeping the encoding time within an acceptable threshold for a smooth user experience is important to reduce the carbon footprint and energy consumption on encoding servers in video streaming applications. Toward this realization, we introduce an encoding latency-a ware dynamic resolution encoding scheme (LADRE) for adaptive video streaming applications. LADRE determines the encoding resolution for each target bitrate by utilizing a random forest-based prediction model for every video segment based on spatiotemporal features and the acceptable target latency. Experimental results show that LADRE achieves an overall average quality improvement of 0.58 dB PSNR and 0.43 dB XPSNR while maintaining the same bitrate, compared to the HTTP Live Streaming (HLS) bitrate ladder encoding of 200 s segments using the VVenC encoder, when the encoding latency for each representation is set to remain below the 200 s threshold. This is accompanied by an 84.17 % reduction in overall encoding energy consumption.

Conventional per-title encoding schemes strive to optimize encoding resolutions to deliver the utmost perceptual quality for each bitrate ladder representation. Nevertheless, maintaining encoding time within an acceptable threshold is equally imperative in online streaming applications. Furthermore, modern client devices are equipped with the capability for fast deep-learning-based video super-resolution (VSR) techniques, enhancing the perceptual quality of the decoded bitstream. This suggests that opting for lower resolutions in representations during the encoding process can curtail the overall energy consumption without substantially compromising perceptual quality. In this context, this paper introduces a video super-resolution-based latency-aware optimized bitrate encoding scheme (ViSOR) designed for online adaptive streaming applications. ViSOR determines the encoding resolution for each target bitrate, ensuring the highest achievable perceptual quality after VSR within the bound of a maximum acceptable latency. Random forest-based prediction models are trained to predict the perceptual quality after VSR and the encoding time for each resolution using the spatiotemporal features extracted for each video segment. Experimental results show that ViSOR targeting fast super-resolution convolutional neural network (FSRCNN) achieves an overall average bitrate reduction of 24.65 % and 32.70 % to maintain the same PSNR and VMAF, compared to the HTTP Live Streaming (HLS) bitrate ladder encoding of 4 s segments using the x265 encoder, when the maximum acceptable latency for each representation is set as two seconds. Considering a just noticeable difference (JND) of six VMAF points, the average cumulative storage consumption and encoding energy for each segment is reduced by 79.32 % and 68.21 %, respectively, contributing towards greener streaming.

VVenC, an open Versatile Video Coding (VVC) encoder, has recently been equipped with rate capping functionality in its two-pass rate control modes, providing constrained variable bitrate coding governed by target rate and maximum rate parameters. This paper reports on implementations and evaluation results of straightforward extensions to VVenC which enable the use of the maximum rate parameter also in the single-pass fixed-QP modes, controlled by a base quantization parameter (QP) instead of a target rate. The rate capping in the fixed-QP mode is achieved, with sufficient accuracy, by evaluating only already calculated pre-processing statistics, thereby avoiding increases in encoder runtime. This encoding mode, given that it supports visual quality optimizations such as XPSNR based block-wise perceptual QP adaptation, can be considered a rate capped constant-quality mode, which was missing in VVenC and which is an interesting configuration for video streaming.

Versatile Video Coding (VVC), the latest video coding standard jointly developed by MPEG and ITU-T [1], has been included in major application standards while first implementations enter the market. The Media Coding Industry Forum (MC-IF) Interoperability WG has published a first release candidate (v1.0-rc) of the VVC technical guidelines for broadcast & streaming applications which aim to [2]:

The Versatile Video Coding (VVC) standard promises high compression efficiency for diverse content types. Based on VVenC, an open and optimized VVC software video encoder, this work presents an inter-frame parallelization (IFP) method designed to exploit the processing power of modern platforms featuring a high number of computing cores. Encoding an ultrahigh definition video on a 32-core machine with the VVenC's faster preset, the proposed method shows more than 20% increase in encoder speed while only a 1% decrease in compression efficiency compared to the default multi-threading mode. In comparison to single-threaded mode, it corresponds to a speedup factor of 18, up from 15x achievable with the previous parallelization scheme. Furthermore, the synergy of the developed inter-frame parallelization technique with other parallelization methods is explored, including tiles and VVC wavefront parallel processing (WPP). The combination of these approaches enables a notable speedup factor of 21, albeit with a trade-off in coding efficiency. With a focus on VVC, this research contributes to the ongoing discourse on video coding optimization, providing valuable insights into possible pitfalls and the potential gains achievable through efficient parallelization techniques on high-core platforms.

Remote desktop applications (RDA) and associated low delay streaming are becoming more important with virtual meetings and processing power in the cloud. Leveraging the Versatile Video Coding (VVC) standard, this paper evaluates and optimizes low-delay screen content coding for RDAs using the open VVC encoder VVenC. First, encoder configuration optimizations are investigated, including hierarchical quantization parameter cascading and motion-compensated temporal pre-filtering. Second, an application is introduced to identify dynamic regions in the desktop frame, facilitating guided fast encoder search optimizations. Experimental results demonstrate that the configuration optimizations and the fast search based on the dynamic regions information achieve notable improvements in coding efficiency (bit-rate reduction of 9.5% for PSNR and 11% for VMAF) and encoding time savings of 31% for VVenC.

Traditional per-title encoding schemes aim to optimize encoding resolutions to deliver the highest perceptual quality for each representation. XPSNR is observed to correlate better with the subjective quality of VVC-coded bitstreams. Towards this realization, we predict the average XPSNR of VVC-coded bitstreams using spatiotemporal complexity features of the video and the target encoding configuration using an XGBoost-based model. Based on the predicted XPSNR scores, we introduce a Quality-A ware Dynamic Resolution Adaptation (QADRA) framework for adaptive video streaming applications, where we determine the convex-hull online. Furthermore, keeping the encoding and decoding times within an acceptable threshold is mandatory for smooth and energy-efficient streaming. Hence, QADRA determines the encoding resolution and quantization parameter (QP) for each target bitrate by maximizing XPSNR while constraining the maximum encoding and/ or decoding time below a threshold. QADRA implements a JND-based representation elimination algorithm to remove perceptually redundant representations from the bitrate ladder. QADRA is an open-source Python-based framework published under the GNU GPLv3 license. Github: https://github.com/PhoenixVideo/QADRA Online documentation: https://phoenixvideo.github.io/QADRA/

Film grain is a distinctive visual characteristic cherished by filmmakers and cinephiles for its ability to evoke nostalgia and artistic aesthetics. However, faithful preservation of film grain during encoding poses unique challenges. Film grain introduces random noise, complicating traditional compression techniques. Consequently, specialized algorithms and encoding strategies have emerged, aiming to strike a harmonious equilibrium. This paper delves into the nuanced realm of film grain handling in Versatile Video Coding (VVC) encoding. We explore the delicate balance between retaining the cinematic charm of film grain and achieving efficient compression. Moreover, we discuss the importance of perceptual quality assessment and adaptive encoding techniques in preserving film grain fidelity. Additionally, we delve into the impact of film grain handling on bitrate control and compression efficiency using VVenC, an open and optimized VVC encoder. Understanding the role of film grain and its nuanced treatment within encoders becomes increasingly pivotal for delivering high-quality, grain-inclusive content in the digital age.

This paper presents and analyzes different parallelization strategies in VVenC, an open and optimized software encoder implementation of the Versatile Video Coding (VVC) standard. VVC has been developed to address the increasing demand for higher compression of digital video data, and it reduces the bitrate by around 50% for the same perceived quality compared to its predecessor, the High-Efficiency Video Coding (HEVC) standard. However, this increase in compression efficiency comes with an increase in computational complexity, particularly on the encoder side. VVenC integrates algorithmic optimizations for each coding tool in VVC and defines a set of five presets from faster to slower that provide Pareto-optimal tradeoffs between runtime and efficiency. Multithreading is employed to further reduce runtime while preserving most of the compression efficiency of each preset. With a hierarchical combination of pre-processing, picture-level and in-picture parallelization, VVenC achieves a 4× speedup for four threads. Further speedup using a higher number of threads depends on the video resolution and used encoder preset. For 16 threads, it ranges from 6 to 9 for high definition to 10-12 for ultra-high-definition video. Compared to previous work, the usage of temporal prediction for the adaptive loop filter reduces the associated coding efficiency loss from 0.4% to almost zero. A better scaling for higher numbers of threads can be achieved at the cost of a higher coding efficiency loss. In the presented framework, increased speedup by smaller block Coding Tree Unit (CTU) sizes, a combination of wavefront parallel processing and various tiles picture partitioning configurations is examined. Furthermore, results on a 20-core ARM-based Apple M1 computer indicate a better scaling for multithreading compared to × 86-based architectures. The analysis is complemented by profiling, which exhibits the overhead by idle threads and identifies the mode estimation as the main bottleneck of the presented framework.

Pareto-front optimization is crucial for addressing the multi-objective challenges in video streaming, enabling the identification of optimal trade-offs between conflicting goals such as bitrate, video quality, and decoding complexity. This paper explores the construction of efficient bitrate ladders for adaptive Versatile Video Coding (VVC) streaming, focusing on optimizing these trade-offs. We investigate various ladder construction methods based on Pareto-front optimization, including exhaustive Rate-Quality and fixed ladder approaches. We propose a joint decoding time-rate-quality Pareto-front, providing a comprehensive framework to balance bitrate, decoding time, and video quality in video streaming. This allows streaming services to tailor their encoding strategies to meet specific requirements, prioritizing low decoding latency, bandwidth efficiency, or a balanced approach, thus enhancing the overall user experience. The experimental results confirm and demonstrate these opportunities for navigating the decoding time-rate-quality space to support various use cases. For example, when prioritizing low decoding latency, the proposed method achieves a decoding time reduction of 14.86% while providing Bjontegaard delta rate savings of 4.65% and 0.32 dB improvement in the eXtended Peak Signal-to-Noise Ratio (XPSNR)-Rate domain over the traditional fixed ladder solution.

This paper presents an in-depth analysis of film grain handling in open-source implementations of the Versatile Video Coding (VVC) standard. We focus on two key components: the Film Grain Analysis (FGA) module implemented in VVenC and the Film Grain Synthesis (FGS) module implemented in VVdeC. We describe the methodologies used to implement these modules and discuss the generation of Supplementary Enhancement Information (SEI) parameters to signal film grain characteristics in the encoded video sequences. Additionally, we conduct subjective and objective evaluations across Full HD videos to assess the effectiveness of film grain handling. Our results demonstrate the capability of the FGA and FGS techniques to accurately analyze and synthesize film grain, thereby improving the visual quality of encoded video content. Overall, our study contributes to advancing the understanding and implementation of film grain handling techniques in VVC open-source implementations, with implications for enhancing the viewing experience in multimedia applications.

Dependent quantization (DQ) is one of the key coding tools in the Versatile Video Coding (VVC) standard. DQ employs two scalar quantizers, with the per-coefficient quantizer selection being governed by a parity-driven 4-state state machine. As the design is normatively enforced, usage of DQ requires a rate-distortion optimized quantization (RDOQ) with a per coefficient decision and state update, e.g. a trellis-based quantization, as initially proposed for the VVC reference software (VTM). Due to its inherent dependencies, including VVCs context selection based on previously encoded coefficient values, and fairly broad search range, the trellis quantization is computationally highly complex. Reducing the complexity of this algorithm is crucial for practical VVC encoders. In this paper we propose a fast dependent quantization trellis search improving the initial design by: trellis pruning of improbable branches, forward adaptive context propagation, and finally a vectorized implementation. The proposed approach, implemented in the open and optimized VVenC encoder, reduces quantization runtime by 37%, allowing an overall 15% encoder speedup in medium preset with no impact on compression performance, in all-intra coding conditions. In random-access conditions, an 9% overall encoder speedup is achieved.

As adaptive streaming becomes crucial for delivering high-quality video content across diverse network conditions, accurate metrics to assess perceptual quality are essential. This paper explores using the eXtended Peak Signal-to-Noise Ratio (XPSNR) metric as an alternative to the popular Video Multimethod Assessment Fusion (VMAF) metric for determining optimized bitrate-resolution pairs in the context of Versatile Video Coding (VVC). Our study is rooted in the observation that XPSNR shows a superior correlation with subjective quality scores for VVC-coded Ultra-High Definition (UHD) content compared to VMAF. We predict the average XPSNR of VVC-coded bitstreams using spatiotemporal complexity features of the video and the target encoding configuration and then determine the convex-hull online. On average, the proposed convex-hull using XPSNR (VEXUS) achieves an overall quality improvement of 5.84 dB PSNR and 0.62 dB XPSNR while maintaining the same bitrate, compared to the default UHD encoding using the VVenC encoder, accompanied by an encoding time reduction of 44.43% and a decoding time reduction of 65.46%. This shift towards XPSNR as a guiding metric shall enhance the effectiveness of adaptive streaming algorithms, ensuring an optimal balance between bitrate efficiency and perceptual fidelity with advanced video coding standards.

Rate control (RC) schemes allow audio and video encoders to produce bitstreams according to specific overall bitrate constraints. However, when no rate capping is enforced, the instantaneous bitrate may vary strongly and may exceed the target rate by an order of magnitude, potentially causing playback stutter especially in video streaming scenarios. This paper introduces a rate capping extension for the two RC modes in VVenC, an open Versatile Video Coding (VVC) compliant encoder implementation. After a revisit of VVenC’s two-pass RC approach, the algorithmic details of the rate capping model are described. The paper concludes with an objective evaluation of the performance of the RC extension in a random-access configuration.