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[ Special Paper ] | |
JOURNAL OF BROADCAST ENGINEERING - Vol. 29, No. 7, pp. 1157-1167 | |
Abbreviation: JBE | |
ISSN: 1226-7953 (Print) 2287-9137 (Online) | |
Print publication date 31 Dec 2022 | |
Received 21 Oct 2024 Revised 18 Nov 2024 Accepted 19 Nov 2024 | |
DOI: https://doi.org/10.5909/JBE.2024.29.7.1157 | |
Analysis of Next-Generation VVC/H.266 Standard for Gaming Content | |
a)Department of Electronics and Information Convergence Engineering, Kyung Hee University | |
b)Department of Electronic Engineering, Kyung Hee University | |
Correspondence to : ‡Kiho Choi E-mail: aikiho@khu.ac.kr Tel: +82-31-750-5799 | |
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
Funding Information ▼ |
Game streaming has emerged as a key technology that enables high-end gaming on various devices by offloading complex processing tasks to remote, high-performance servers. However, delivering high-quality game streams with minimal latency and high compression efficiency presents several challenges in video compression. This paper evaluates the impact of key coding tools in the Versatile Video Coding (VVC)/H.266 standard on gaming content. Experimental results show that tools such as Intra Block Copy (IBC) and Linear Model for Chrominance intra prediction (LMChroma) provide excellent compression performance for screen content sequences, while Chroma Transform Skip (ChromaTS) and Joint Cb and Cr (JCCR) exhibit limited performance due to differences in chroma structure. These findings highlight the need for next-generation coding standards to more effectively reflect the unique characteristics of gaming content, ensuring high-quality, low-latency streaming.
Keywords: VVC/H.266, Gaming contents, Video Coding |
Game streaming is the practice of playing video games that are rendered and processed on a remote server and then broadcast to the player in real time via the internet. Instead of running games on local hardware, users utilize a client device (e.g., PC, console, smartphone, etc.) to connect to games hosted on powerful servers, allowing even low-end devices to execute graphically complex games[1].
Thanks to services such as Google Stadia, Microsoft xCloud, NVIDIA GeForce Now, and Sony PlayStation Now, game streaming has become more and more popular. These services process games on distant servers, streaming the visual feed to the user’s device and sending real-time player inputs from a mouse, keyboard, or controller back to the server[2].
The global cloud gaming market is growing rapidly, driven by increased internet penetration, the rise of mobile gaming, and the development of 5G networks. According to market reports, the cloud gaming market size was valued at $1.6 billion in 2021, and it is expected to reach $13.7 billion by 2028, growing at a Compound Annual Growth Rate (CAGR) of around 40-50% over the forecast period[3]-[4].
However, alongside this rapid market growth comes the challenge of ensuring a smooth and responsive gaming experience. To achieve this, cloud gaming services must overcome issues such as latency, which is critical for real-time gameplay. This requires the use of advanced video compression technologies that can reduce data size while preserving high-quality visuals and minimizing lag, enabling the seamless delivery of graphically intense games even over bandwidth-limited connections.
To address these challenges, standard bodies have recently begun studying video coding technologies dedicated to future applications in gaming content. In particular, [5] proposed the creation of a new class of gaming sequences for the common test conditions used in the development of next-generation video coding standards. Building on this suggestion, numerous proposals have emerged, aiming to support critical new applications and incorporate gaming-specific test conditions, reflecting the evolving demands of video coding for high-performance gaming environments.
In this paper, we explore the latest advancements in video coding standard, specifically focusing on the key tools of Versatile Video Coding (VVC)/H.266[6]. The investigation assesses how these tools can be effectively applied to sequence of gaming contents, which are anticipated to play a significant role in the industry. By examining their compatibility and performance with gaming content, we provide an insight into how VVC/H.266’s advanced features can meet the growing demands of high-quality, low-latency game streaming[7].
The remainder of this paper is structured as follows: Section II provides a review of video coding standards as applied to gaming content. Section III and IV presents an examination and evaluation of the coding performance of individual VVC/H.266 tools on game sequences. Sections V and VI analyze the testing results and offer concluding this paper.
The quality of game streaming heavily relies on effective video compression technology. Video compression reduces the amount of data required to represent video content, which is essential for streaming games that feature fast-paced, high-resolution graphics. By compressing video efficiently, these technologies help minimize bandwidth usage, reduce latency, and ensure a seamless and smooth gaming experience.
The codec standard code below is available for streaming games:
Nevertheless, all of the codecs mentioned, including H.264, H.265, VP9, AV1, and even VVC/H.266, were primarily developed with natural camera-captured content in mind, as these have been the dominant video applications. While VVC/H.266 did consider artificial content, such as gaming and screen content, during its development, its primary focus remained on optimizing for natural video. As a result, most of the tools and functionalities were designed for camera-captured video, with artificially generated content being a secondary consideration.
To better accommodate the unique characteristics of gaming content, the next-generation video coding standard must thoroughly analyze existing tools and identify areas for further improvement. In this paper, we examine key tools in VVC/H.266, using gaming content proposed for the Post-VVC/H.266 exploration experiments, in preparation for the development of future video coding standards. This analysis aims to assess how well current tools perform and what enhancements are needed to optimize compression for gaming content.
The testing conditions are based on the draft version of the common test conditions (CTC) for gaming content, currently being developed by JVET[12]. To simplify the process, we selected four gaming sequences specified in the draft CTC. Details of the specific content used in the tests are provided in Table 1, while Figure 1 displays sample screenshots from the tested gaming sequences.
Class | Sequence name | Resolution | Frame count | Low delay frame count |
Frame rate | Bit depth |
---|---|---|---|---|---|---|
G1 | Level1_SDR | 1920x1080 | 600 | 300 | 60 | 10 |
Darktree_SDR | 1920x1080 | 600 | 300 | 60 | 10 | |
G2 | GTAV | 1920x1080 | 600 | 300 | 60 | 8 |
Minecraft | 1920x1080 | 600 | 300 | 60 | 8 |
To assess the effectiveness of individual coding tools, we performed turn on/off testing based on the default setting of VVC/H.266 CTC. For tools enabled by default, we conducted tests by disabling them, and for tools not enabled by default, we tested by enabling them. To quantify the results, we measured the changes in BD-rate[13] compared to the anchor, which used the default settings. For simplicity, we utilized AI configurations with four selected gaming sequences from the draft CTC for gaming content. These sequences were compressed using the reference software VTM-11.0[14]. The coding parameters followed the CTC[12], established by JVET for VVC/H.266 standardization. Quantization parameters (QPs) of 22, 27, 32, 37, 42, and 47 were used to evaluate changes in BD-rate
In evaluating the intra prediction tools[15] for game streaming, we tested Intra Subpartition (ISP), Multiple Reference Line (MRL), Matrix-based Intra Prediction (MIP), and Intra Block Copy (IBC). Since ISP, MRL, and MIP are enabled by default, we conducted tool-off tests for these tools. For IBC, which is disabled by default, we performed a tool-on test to assess its impact. In the tool-off tests, a positive number in the results indicates an increase in bitrate at the same PSNR quality, showing the impact of disabling the tool. By examining the increase in BD-rate during tool-off tests, we can evaluate the contribution of each tool compared to the default settings. Conversely, in the tool-on test, a negative number indicates a decrease in bitrate at the same PSNR quality, allowing us to measure the benefit of enabling the tool relative to the default configuration.
In VVC/H.266, ISP tool enhances compression efficiency by dividing a coding block into smaller sub-blocks, allowing each sub-block to be predicted and encoded separately. This approach is especially beneficial for complex textures and detailed areas, as it enables more accurate intra predictions and reduces prediction errors.
As shown in Table 2, the performance changes by 0.52%, -0.36%, and 0.06% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off testing results suggest that the impact of disabling ISP is relatively comparable to the results observed in natural camera-captured videos[16]. This consistency highlights that ISP’s contribution to compression efficiency in gaming content is similar to its role in conventional video content.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 0.51% | -0.36% | 0.41% |
Darktree | 0.64% | -0.94% | -0.72% | |
G2 | GTAV | 0.41% | 0.49% | 0.34% |
Minecraft | 0.53% | 0.30% | 0.22% | |
Average | 0.52% | -0.13% | 0.06% |
MRL tool in VVC/H.266 enhances intra prediction by utilizing multiple reference lines from neighboring blocks instead of relying on one. This approach enables more precise predictions by capturing a wider range of directional patterns, which is particularly beneficial for encoding complex textures. By improving prediction accuracy, MRL helps reduce residual errors, leading to better overall compression efficiency.
As shown in Table 3, the performance changes by 0.30%, 1.85%, and 0.98% for the Y, Cb, and Cr components on average, compared to the default settings. Overall, the tool-off test results indicate that the impact of disabling MRL is quite similar to what is observed in natural camera-captured videos[16]. Notably, MRL has a significant impact on the Minecraft sequence, suggesting that the use of multiple reference lines for intra prediction is highly beneficial even in gaming content, where additional directional references can greatly enhance compression efficiency.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 0.16% | 0.73% | 1.12% |
Darktree | 0.15% | 0.17% | -0.12% | |
G2 | GTAV | 0.19% | 0.39% | 0.58% |
Minecraft | 0.69% | 6.11% | 2.34% | |
Average | 0.30% | 1.85% | 0.98% |
MIP tool in VVC/H.266 introduces an innovative approach to intra prediction by using matrix-based transformations to predict pixel values. This method is particularly effective for blocks with non-linear or complex textures, where conventional directional prediction methods may be less accurate. MIP enhances prediction precision in such cases, leading to improved compression efficiency.
As shown in Table 4, the performance changes by 0.64%, 2.04%, and 0.99% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results indicate that the impact of disabling MIP is similar to that observed in natural camera-captured videos, with a slight improvement in Chroma coding performance[16].
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 0.70% | 1.60% | 1.18% |
Darktree | 0.52% | 0.22% | 0.10% | |
G2 | GTAV | 0.55% | 0.59% | 0.59% |
Minecraft | 0.80% | 5.76% | 2.10% | |
Average | 0.64% | 2.04% | 0.99% |
IBC tool in VVC/H.266 is designed for compressing screen content by reusing previously reconstructed blocks within the same frame. It functions similarly to inter prediction but operates within the same frame, allowing for efficient encoding of repeating patterns, text, or graphics. By leveraging existing data within a frame, IBC significantly improves compression efficiency for screen content applications, reducing the need for redundant coding.
As shown in Table 5, the performance changes by -3.37%, -2.84%, and 0-2.98% for the Y, Cb, and Cr components on average, compared to the default settings. Since IBC is disabled by default in the AI configuration, we conducted a tool-on test to assess its impact. The results demonstrate a significant reduction in BD-rate, indicating that enabling IBC leads to notable improvements. Given that IBC is designed to provide accurate predictions for screen content, it is reasonable to observe such substantial performance gains in gaming sequences.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | -0.04% | 0.05% | 0.06% |
Darktree | -0.24% | 0.06% | -0.18% | |
G2 | GTAV | -0.86% | -0.68% | -0.70% |
Minecraft | -12.33% | -10.78% | -11.11% | |
Average | -3.37% | -2.84% | -2.98% |
In evaluating the Transform tools[17] for game streaming, we tested Intra Transform Skip (TS), Multiple Transform Selection (MTS), and Low-Frequency Non-Separable Transform (LFNST) are enabled by default, we conducted tool-off tests for these tools.
TS tool in VVC/H.266 allows certain blocks of residual data to bypass the transform stage, which normally converts spatial domain data to the frequency domain. This is particularly useful for small blocks or regions with simple textures, where applying a transform may not provide compression gains.
As shown in Table 6, the performance changes by 2.01%, 2.26%, and 1.38% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results show a significant reduction in BD-rate, particularly for the Minecraft sequence. Given that Transform Skip is designed to handle screen content where the prediction closely matches the original, it is reasonable to observe such notable improvements in gaming content.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 0.66% | 0.55% | 1.07% |
Darktree | 0.44% | -0.25% | -0.59% | |
G2 | GTAV | 0.86% | 0.37% | 0.70% |
Minecraft | 6.09% | 8.37% | 4.33% | |
Average | 2.01% | 2.26% | 1.38% |
MTS tool in VVC/H.266 enhances the compression process by offering a selection of different transform types including DCT-II and DST-VII to better adapt to varying block characteristics. By selecting the most suitable transform for each block, MTS improves the representation of both smooth and detailed areas, leading to more efficient residual compression.
As shown in Table 7, the performance changes by 0.83%, 1.18%, and 0.50% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results indicate that the impact of disabling MTS is similar to that observed in natural camera-captured videos[16]. However, an interesting result was observed in the Minecraft sequence. This can be explained by the characteristics of Minecraft, which are more aligned with screen content, while the other sequences resemble natural content in their transform usage, leading to different results across sequences.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 0.97% | 2.38% | 0.50% |
Darktree | 1.01% | 0.90% | 0.80% | |
G2 | GTAV | 1.38% | 1.27% | 0.82% |
Minecraft | -0.06% | 0.19% | -0.14% | |
Average | 0.83% | 1.18% | 0.50% |
LFNST tool in VVC/H.266 improves compression efficiency by applying an additional transform to low-frequency components of intra-predicted blocks. This tool is designed to capture residual energy more effectively in smooth or flat regions, where traditional transforms may not provide optimal compression. By refining the representation of low-frequency details, LFNST helps reduce the overall bitrate while preserving video quality, particularly for high-resolution and low-texture areas.
As shown in Table 8, the performance changes by 0.45%, 1.40%, and 1.81% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results indicate that disabling LFNST has a smaller impact compared to natural camera-captured videos[16]. This may be due to the more uniform residual patterns in gaming sequences, which differ from the more varied textures in natural content. As a result, the additional transform applied by LFNST is less effective in gaming sequences, where intra-prediction and core transforms are already highly efficient.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 0.43% | 1.61% | 2.67% |
Darktree | 0.43% | 1.80% | 2.26% | |
G2 | GTAV | 0.50% | 2.00% | 1.42% |
Minecraft | 0.43% | 0.20% | 0.88% | |
Average | 0.45% | 1.40% | 1.81% |
In evaluating the In-loop filtering tools[18] for game streaming, we tested Sample Adaptive Offset (SAO), Adaptive Loop Filter (ALF), and Luma Mapping with Chroma Scaling (LMCS) are enabled by default, we conducted tool-off tests for these tools.
SAO tool, carried over from HEVC to VVC/H.266, enhances video quality by reducing artifacts and improving edge preservation after the primary compression process. It works by applying offsets to reconstructed pixels based on the characteristics of neighboring pixels, thus correcting distortion introduced during block-based compression. SAO effectively improves visual quality while maintaining compression efficiency, making it useful for both high-resolution and detailed content.
As shown in Table 9, the performance changes by 0.15%, 0.37%, and 0.19% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results indicate that the impact of disabling SAO is similar to what is observed in natural camera-captured videos[16]. This outcome is expected, as SAO was primarily designed to enhance the visual quality of reconstructed video rather than to improve objective coding performance. However, the results suggest that SAO may be particularly beneficial for artificially generated content, as evidenced by the improved coding performance observed in the Minecraft sequence.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | -0.04% | -0.10% | 0.03% |
Darktree | 0.08% | 0.03% | -0.01% | |
G2 | GTAV | 0.07% | 0.25% | 0.01% |
Minecraft | 0.51% | 1.30% | 0.75% | |
Average | 0.15% | 0.37% | 0.19% |
ALF tool in VVC/H.266 refines the quality of reconstructed video frames by applying a filter after the decoding process, targeting specific areas to reduce artifacts and improve visual quality. ALF dynamically adjusts its filtering based on the content of each frame, enhancing edge detail and reducing block artifacts introduced during compression. This tool helps improve overall video quality while maintaining compression efficiency, particularly for high-resolution and complex content.
As shown in Table 10, the performance changes by 1.90%, 14.06%, and 13.21% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results indicate that the impact of disabling ALF is smaller than what is observed in natural camera-captured videos[16]. However, the coding improvement in the Chroma components is particularly noteworthy, demonstrating ALF’s effectiveness in enhancing color accuracy and compression efficiency.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 1.05% | 20.40% | 6.07% |
Darktree | 1.41% | 5.17% | 5.86% | |
G2 | GTAV | 1.64% | 12.94% | 32.30% |
Minecraft | 3.50% | 17.71% | 8.61% | |
Average | 1.90% | 14.06% | 13.21% |
LMCS tool in VVC/H.266 enhances video quality by adjusting the brightness (i.e., Luma) and color (i.e., Chroma) components separately to better match the visual characteristics of the content. It applies dynamic scaling to luma values, which allows for more efficient encoding of scenes with varying brightness levels, while simultaneously ensuring that the chroma components are adjusted accordingly.
As shown in Table 11, the performance changes by 0.15%, 0.02%, and 0.08% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results indicate that the impact of disabling LMCS is smaller than what is observed in natural camera-captured videos[16]. Notably, the sequences in the G1 class show no coding improvements for either Luma or Chroma components, suggesting that the effectiveness of LMCS may vary depending on the characteristics of the content.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 0.00% | 0.00% | 0.00% |
Darktree | -0.08% | -0.42% | -0.25% | |
G2 | GTAV | 0.16% | -0.36% | -0.97% |
Minecraft | 0.53% | 0.85% | 1.56% | |
Average | 0.15% | 0.02% | 0.08% |
In this section, we conducted separate tests for Linear Model for Chrominance intra prediction (LMChroma)[19], Joint Cb and Cr (JCCR)[20], and Chroma Transform Skip (ChromaTS)[21], as these tools are primarily designed to enhance the compression of chroma components. Since the chroma characteristics in gaming content differ from those in natural videos, it is important to evaluate these chroma-focused tools independently. This allows us to better understand their performance and efficiency in handling the unique properties of gaming sequences.
LMChroma tool in VVC/H.266 improves chroma (e.g., color) prediction accuracy by utilizing information from the luma (e.g., brightness) component to enhance chroma prediction. This tool exploits the correlation between luma and chroma components, adjusting chroma values based on the luma content within a block. By doing so, LMChroma enhances color representation and reduces residual errors, leading to better compression efficiency and visual quality, especially in scenes with complex color textures.
As shown in Table 12, the performance changes by 2.21%, 69.63%, and 42.25% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results demonstrate significant improvements in both Luma and Chroma components, with particularly impressive gains in Chroma performance. This suggests that the relationship between Luma and Chroma in gaming content is stronger than in natural camera-captured videos, potentially allowing these tools to achieve more effective compression in gaming sequences.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 3.81% | 55.24% | 17.17% |
Darktree | 0.64% | 55.43% | 70.76% | |
G2 | GTAV | 1.65% | 91.06% | 25.73% |
Minecraft | 2.75% | 76.78% | 55.33% | |
Average | 2.21% | 69.63% | 42.25% |
JCCR tool in VVC/H.266 enhances compression efficiency by jointly coding the chroma components (i.e., Cb and Cr), rather than treating them independently. This tool leverages the correlation between the two chroma channels, allowing for more efficient prediction and reduced residual data.
As shown in Table 13, the performance changes by 0.23%, 1.03%, and 0.34% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results indicate that the impact of disabling JCCR is smaller than what is typically observed in natural camera-captured videos[16]. This suggests that the benefits of JCCR in gaming content may be less pronounced, possibly due to differences in the chroma-luma relationships between gaming sequences and natural video content.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | 0.41% | 1.36% | -0.65% |
Darktree | 0.06% | 1.10% | 2.43% | |
G2 | GTAV | 0.23% | 2.21% | 0.40% |
Minecraft | 0.23% | -0.54% | -0.83% | |
Average | 0.23% | 1.03% | 0.34% |
ChromaTS tool in VVC/H.266 improves compression efficiency for chroma components by allowing certain chroma blocks to bypass the transform stage, similar to the transform skip for luma. This is particularly useful for chroma blocks with simple or flat color regions, where a transform may not offer significant gains. By skipping the transform in these cases, ChromaTS helps reduce unnecessary complexity and preserve color details, optimizing compression for specific content types.
As shown in Table 14, the performance changes by 0.00%, 1.59%, and 0.90% for the Y, Cb, and Cr components on average, compared to the default settings. The tool-off test results suggest that disabling ChromaTS has a relatively small impact on gaming content compared to TS tool. This indicates that the current design of ChromaTS may not align well with the characteristics of chroma components in gaming sequences, limiting its effectiveness in this context.
Class | Sequence | BD-rate | ||
---|---|---|---|---|
Y | U | V | ||
G1 | Level1 | -0.01% | 0.61% | 1.10% |
Darktree | 0.00% | 0.03% | 0.03% | |
G2 | GTAV | -0.02% | 0.10% | 0.38% |
Minecraft | 0.04% | 5.60% | 2.08% | |
Average | 0.00% | 1.59% | 0.90% |
The evaluation of key video coding tools within the VVC/H.266 framework provides valuable insights into their suitability and effectiveness for game streaming applications. Tools such as ISP, MRL, MIP, IBC, and TS showed varying degrees of performance based on the unique characteristics of gaming content.
The results indicate that some tools, such as MRL and IBC, are particularly well-suited for gaming sequences for example Minecraft, which align more closely with screen content patterns. These tools efficiently leverage repetitive textures and structured patterns, resulting in significant coding gains. Conversely, other tools, such as ChromaTS and LFNST, demonstrate reduced effectiveness, likely due to the distinct characteristics in gaming content compared to natural video.
Interestingly, the tests show that tools such as JCCR and ChromaTS, which primarily target chroma components, offer only limited improvements in gaming sequences. This suggests that the chroma structures in gaming content may not fully align with the assumptions underlying these tools, reducing their impact. However, the impressive gains observed in tools such as IBC and LMChroma confirm the need for specialized coding strategies dedicated to artificially generated content.
These findings highlight a critical gap in existing video coding tools, which were predominantly developed for natural camera-captured content. Although VVC/H.266 introduces more adaptable tools, further refinements are needed to optimize for the specific characteristics of gaming content. As game streaming continues to grow, future video coding standards must address these unique challenges, ensuring that compression tools can efficiently handle complex, high-resolution gaming sequences.
This paper presents an evaluation of key tools in VVC/H.266 to determine their effectiveness in compressing gaming content. The results reveal that tools like ISP, MRL, and IBC contribute meaningfully to compression efficiency, particularly in sequences with repetitive patterns or screen content. However, other tools, such as ChromaTS and JCCR, demonstrate limited effectiveness, suggesting that current coding strategies may not fully align with the characteristics of gaming sequences.
The analysis highlights the need for further research and development in video coding technologies to better accommodate the unique properties of gaming content. As game streaming becomes increasingly prominent, future standards must consider these requirements to ensure efficient compression and high-quality streaming. Overall, while VVC/H.266 offers promising improvements, continued efforts are essential to refine these tools and develop new coding strategies tailored to the evolving demands of game streaming.
This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2023-00220204, Development of Standard Technologies on next-generation media compression for transmission and storage of Metaverse contents).
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- 2017. 09. ~ 2021. 06. : B.S. degree in Digital Media Technology Specialty, Yanbian University
- 2023. 09. ~ Current : Master Student, Electronics and Information Convergence Engineering, Kyung Hee University
- ORCID : https://orcid.org/0009-0000-0122-563X
- Research interests : Video coding and compression technologies,improving video compression efficiency and quality. Artificial Intelligence in Video Processing,AI-driven video processing and search technology. Versatile video search technology,using AI to optimize the search performance of large-scale video data.
- 2012. : Electronics and computer engineering, Hanyang University, B.S.(2008), Ph.D.(2012)
- 2012. 09. ~ 2014. 02. : Lecturer, Post Doc., Hanyang University
- 2014. 03. ~ 2021. 02. : Senior Engineer, Visual Technology Lab. of Samsung Research
- 2021. 03. ~ 2023. 02. : Professor, School of Computing, Gachon University
- 2023. 03. ~ Current : Professor, Electronic Engineering, Kyung Hee University
- ORCID : https://orcid.org/0000-0002-2869-0440
- Research interests : Image and video coding, multimedia data compression, multimedia streaming, AI-based multimedia technology, and multimedia standardization