Hevc class b dataset. The scope of this group was .

Hevc class b dataset We present a dataset that contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. When you use it, you can split the image into several 64x64 images or 32x32 and so on. Our method is simple yet e ective and outperforms the state-of-the-art learning based video codecs on benchmark datasets without increasing the model size or decreasing the decoding speed. 96% with the HEVC-class B dataset, and outperforms Delta rate savings on conventional datasets UVG, HEVC Class B and MCL-JCV, outperforming the baseline CANF-VC and even the H. 200 bpp 0. 01% with the UVG dataset, 50. We evaluate the R-D performance using LPIPS and compute the BD-rate savings for Firstly, we only reuse the ConvNeXt block for once, and evaluate the compression performance on the HEVC Class B dataset. The video processed by DVC has a resolution limit, and here you need to manually crop the video to a multiple of 64. 80dB@ 0. The learning-based codecs Li(CVPR020) [7], Lu(ECCV020) [8], High Efficiency Video Coding (HEVC) is the current joint video coding standardization project of the ITU-T Video Coding Experts Group (ITU-T Q. 265/HEVC [5] encoder propos-als [1]. Comparisons between the proposed model with the Agustsson’s , Habibian’s , DVC , HLVC , Wu’s , H. 10. In literature, a large number of algorithms [37,28] HEVC Class B dataset Proposed Proposed (w/o BA) DVCPro* DVC* H. Our codec shows clear advantages over recent VCM methods such as SMC and DeepSVC: (1) Both SMC and DeepSVC require multiple decoders for different tasks, whereas our method necessitates only a single pre-trained The Kodak , JCT-VC (HEVC Class B, C and D) We provide a qualitative comparison of Kodak and HEVC Class C datasets between I 2 VC and other methods [25, 33, 39, 61] as depicted in Figure 6. 266/VVC [6] The UVG dataset and the HEVC dataset have 12 and 10 GOP sizes, respectively. e. Peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index (MS-SSIM) [ 23 ] are utilized to quantitatively assess video quality, and bit per pixel (bpp) measures bit allocations for motion and residual. 265 (veryslow) H. We have the experiments of e xcluding these. The resolution of Class B is 1920 × 1080, of Class C is 832 × 480, of Class D is 416 × 240 and of Class E is 1280 × 720. For LD and RA As shown in HEVC Class C dataset HEVC Class C dataset 34 6 33 32 31 M=25 M=4 M=1 30 0. At the end HEVC Class B sequences, and all frames were compressed with a GOP size of 12 for the UVG dataset and MCL-JCV dataset. 125BPP H. 9875 0. 5 Conclusion. also cause artifacts with limited performance The dataset SFU-HW-Objects-v1 contains bounding boxes and object class labels for High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) video sequences. also cause artifacts with limited performance Optical flow with statistical analysis. Following previous schemes, our processing time contains encoding time and decoding time. Results for DCVC [5] and M- LVC [6] are produced by their released code. Figure 5: Rate–distortion performance on the MCL-JCV dataset , UVG dataset , and HEVC Class B dataset . For each video frame, ground truth annotations include object class ID, Download scientific diagram | Detailed rate-distortion performance of B-EPIC(MSE) on the UVG dataset. Rate-distortion performance The benchmarks section lists all benchmarks using a given dataset or any of its variants. Download scientific diagram | Detailed rate-distortion performance of B-EPIC(MS-SSIM) on the UVG dataset. that in practical scenarios. On datasets with higher resolution videos (UVG dataset, HEVC Class B dataset, and HEVC Class E dataset), our network was able to outperform the latest H. 3 Bpp 0. These datasets provided a range of resolutions and content complexities to measure M3-CVC’s performance. You can test other common datasets such as UVG in a similar way. 4 0. Most on-air broadcast UHD services will not start until 4K display This paper provides an overview of our open Ultra Video Group (UVG) dataset that is composed of 16 versatile 4K (3840×2160) test video sequences. 9900 MS-SSIM HEVC Class E dataset Proposed Proposed (w/o BA) DVCPro* DVC* H. It can be observed that our method outperforms DVC [5] by The second line is the ParkScene sequence from HEVC Class B dataset compressed to around 0. In this section, we also compare our proposed method with the traditional video compression algorithms H. 265 [5] , as well as the deep learning-based method DVC [13] . Experimental results show the proposed framework outperforms the SOTA deep neural video compression networks on the HEVC- class B dataset and is competitive on the UVG and MCL- JCV datasets. Test Settings. As for sequences with simple and slow motions, such as the three To comprehensively verify the wide coverage of our proposed dataset for various content elements and qualitatively analyze the superiority of USTC-TD, we evaluate the USTC-TD on different image/video features and compare it with the previous image/video common test datasets (image datasets: Kodak , CLIC , Tecnick , video datasets: HEVC CTC , VVC CTC , What's in the dataset? In HEVC intra-prediction, each I-frame is divided into 64x64 Coding Tree Units (CTU). Class B is five 1080p natural video sequences, We assess the algorithm on the UVG dataset and the HEVC Standard Test Sequences to report on its performance (Class B, Class C, Class D and Class E). Experimental results show the proposed framework improves the coding efficiency of the base deep neural model by 48. 5(b), we take the HEVC Class E dataset as an example and show the average PSNR results over all predicted frames (i. 5 Previous frame Current frame Proposed 32. 4. 96% with the HEVC-class B dataset, and outperforms the previous deep neural video compression networks. 29 We also compare against VVC on the datasets of UVG, HEVC ClassB and HEVC ClassE, as shown 30 in Fig. Python (3. For each 64x64 CTU, there's a depth prediction represented by a 16x16 matrix. We conducted a comparative analysis of our method with existing video codecs, including H. The scope of this group was extended to continue Experimental results show the proposed framework improves the coding efficiency of the base deep neural model by 48. In literature, a large number of algorithms [37,28] Experimental results show the proposed framework outperforms the SOTA deep neural video compression networks on the HEVC-class B dataset and is competitive on the UVG and MCL-JCV datasets. @inproceedings{lu2024deep, title={Deep Hierarchical Video Compression}, author={Lu, Ming and Duan, Zhihao and Zhu, Fengqing and Ma, Zhan}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence I need the HEVC test video sequences, namely Traffic, PeopleOnStreet, Nebuta, SteamLocomotive, Kimono, ParkScene, Cactus, BasketBallDrive etc mentioned in Common Conditions and Software Reference Configuration’s document JCT-VCL1100. In addition, it generates high-quality reconstructed frames in comparison with conventional motion coding-based symmetric auto-encoder meanwhile its model Test dataset. 265 in The researchers tested the codec on datasets widely used in video codec analysis, including the UVG dataset, the MCL-JCV dataset, and HEVC Class B and Class C datasets. 4 Comparison with ELF-VC Visual results of each step in generating perceptual relevant saliency mask a representative frames b saliency map generated by context based visual saliency model c binary masks after thresholding (\(\beta =9\)) d human labeled ground truth for video sequences e CTU based perceptual relevance mask for video sequences first Row: ParkScene (class B), second . The findings, presented in Table 1, reveal that the upgraded HiNeRV network with parameter reuse exceeds the performance of the original network obviously. 88%, 2. Implementation Details. 050 0. 96% with the HEVC-class ET) developed the oficial test sequences to evaluate H. Compared with VVC, our method performs worser in low bit-rate but better in high bit-rate 31 when evaluated by MS-SSIM. Fine-tuned PWC-Net. 266 reference software The bpp and PSNR values are averaged on each class. Save Add a new evaluation result row ×. \(\bar{X}_t\) ’s) after motion compensation at different Bpps. The dataset can be used to Figure 2: An example from BasketballDrive in HEVC Class B dataset. The GOP size is 30, and tested frame number is 60. 06%, 33. This repository contains our series of works on Deep Hierarchical Video Compression. 9725 0. For one CTU, which There are two python files, rd_plot_UVG. 08. I need these video sequences for benchmarking and research purposes. Negative values in BDBR indicate bit-rate savings. Ultra High Definition video is entering a phase of deployment tests, with first UHD services being launched in late 2013. Performance across models -MS-SSIM vs Rate (bits-per-pixel) Video Rate MS-SSIM Rate We employ the Vimeo-90K dataset for training, and the HEVC Class B , UVG , and MCL-JCV datasets for inference. (a) and (b) represent two consecutive frames from the HEVC Class C dataset, (c) and (e) represent the raw optical flow and its flow magnitude, (d) and (f HEVC Class B dataset. Although using different intraframe coders (see Download scientific diagram | RD curves of PSNR and MS-SSIM on UVG, MCL-JCV, and HEVC Class B datasets. 8k次，点赞3次，收藏9次。在进行HEVC快速算法研究中，经常要做测试。拿哪个序列做测试是一个问题，总不能每次都对所有的序列测试，这样的话工作量太大了。经过阅读文献与经验的总结，个人认为做测试的序列应满足如下条件：第一：每种类别的序列都应该涉及在“Common HM test Experiment results show that our method can significantly outperform the previous state-of-the-art (SOTA), and our neural codec can achieve -7. The dataset format can be seen in dataset_config_example. It is one of the datasets used in the MPEG-VCM (Video Coding for Machines) standardization group. This data set may be used to simulate UHD DASH services, whether on-demand or live, using real-life professional quality content. near-optimal and it only requires 3 iterations to obtain the optimal parameters. A. This insight underscores the proposed methods’ capability to improve rate control accuracy without introducing significant time latency, making CANF-VC++ achieves significant Bj{\o}ntegaard-Delta rate savings on conventional datasets UVG, HEVC Class B and MCL-JCV, outperforming the baseline CANF-VC and even the H. Labels: The labels are in the pkl folder. 265, DVC [5], and the proposed method. Citation. 3. There are only a few di erences between the interpolated frame and the input frame that need to be transmitted. Zhihao Hu Dong Xu Guo Lu Wei Jiang DVC: An End-to-end Deep Video Compression Framework, CVPR 2019 (Oral) - GuoLusjtu/DVC It saves an average of 11. Download scientific diagram | Rate-distortion performance evaluation on UVG, HEVC Class B, and MCL-JCV datasets for both PSNR-RGB and MS-SSIM-RGB. On the encoding side, M3-CVC shows notable promise. For example, ImageNet 32⨉32 and ImageNet 64⨉64 are variants of the ImageNet dataset. HEVC Class E dataset), our network was able to outperform. The DCVC obtains further higher performance than x265 anchor by 14. 5 (c) Figure 7: Ablation Comparison of our method with H. In [39], the pre-trained PWC-Net. from publication: Extending Neural P-frame Codecs for B-frame Coding | While most neural video In this paper we present datasets for both trace-based simulation and real-time testbed evaluation of Dynamic Adaptive Streaming over HTTP (DASH). Compression Performance on the HEVC Class C and Class D In the submitted paper, we provide the performance on the HEVC Class B and Class E datasets. In this section, we show the experimental results of the. Currenlty the spatial resolution of video needs to be cropped into the integral times of 64. 4b), our models show comparable performance to x265 at both high rates and low rates, resulting in 1% overall rate reductions with the singlerate model and 7. Expand [PDF] 1 Excerpt; Save. comparison between standard Nov 26, 2024 · The UVG dataset and the HEVC dataset have 12 and 10 GOP sizes, respectively. These natural sequences were captured either at 50 or 120 frames per second (fps) and stored online in raw 8-bit and 10-bit 4:2:0 YUV formats. Lower is better. The compared methods are H. Result of each step in our method applied to HEVC and VVC. 3 R-D curves on the HEVC Class B and Class E datasets. 264 and H. Firstly, we will describe how to implement the whole method, and then we will introduce the datasets we used. 266 reference software Figure 4: An example from BasketballDrive in HEVC Class B dataset. Please refer to our paper for more experimental results. 59 dB PSNR gains, demonstrating that conditional coding method DCVC can The test process here is based on the class B in the HEVC test sequence. There are only a few differences between the interpolated frame and the input frame that need to be transmitted. 99%, and 51. Previous frame Current frame Proposed 28. Index Terms—Deep Learning, Video Compression To validate the effectiveness of the methods employed in our framework, we conduct a series of ablation experiments using VTM-17. The MCL-JCV consists of 30 videos, where each consists of approximately 150 frames. 18dB @0. 4 33 5 PSNR(dB) Proportion of voxel flows (%) 34 PSNR(dB) HEVC Class C dataset 4 M=25 w/o GFP M=25 M=4 M=1 3 0. 2 0. from publication: Extending Neural P-frame Codecs for B-frame Coding | While most neural In this paper we present a Multi-Profile Ultra High Definition (UHD) DASH dataset composed of both AVC (H. The elements in the matrix are 0, 1, 2 or 3, indicating depth 0/1/2/3 for a High Efficiency Video Coding (HEVC) is the current joint video coding standardization project of the ITU-T Video Coding Experts Group (ITU-T Q. Although using different intraframe coders (see The scope of this group was extended to continue working on Format Range Extensions (RExt), Scalable HEVC (SHVC) and Screen Content Coding (SCC) as extensions of HEVC. In Fig. For Experimental results show the proposed framework outperforms the SOTA deep neural video compression networks on the HEVC-class B dataset and is competitive on the In this work we present datasets for both trace-based simulation and real-time testbed evaluation of Dynamic Adaptive Streaming over HTTP (DASH). It can be observed that our method outperforms DVC [5] by Nov 15, 2023 · Class B is five 1080p natural video sequences, Class C is four WVGA natural sequences, Class D is four WQVGA natural video sequences, and Class E is three 720p high-quality video sequences of people talking and 4 videos of screen content of various resolutions. 265 33. Ground-truth annotations for 13 sequences were prepared and released as the dataset called SFU-HW-Tracks-v1. 10(f) shows that our TCM still outperforms DeepSVC in Bpp-PSNR metric. The main goal of the HEVC standardization effort is to enable significantly improved compression performance relative to existing standards-in the range of 50% bit-rate On HEVC Class B dataset (Fig. 9775 0. 01: Upload PyTorch implementation of DVC: An End-to-end Deep Video Compression Framework For reducing HEVC encoding complexity through deep learning based approach, a large-scale database for CU partition of HEVC (CPH) is established, for both intra- and inter-modes. It is worth mentioning that our design achieves significant compression efficiency improvement on the HEVC Class B dataset, especially on the BasketballDrive DATASET 3: Header Dataset - The Header or Dummy dataset, which can be found here, is composed of the header information from each of our two hundred and thirty evaluated clips (twenty three clips * two encoders * five segment durations). 264/AVC []. 264 [4] , H. There are 47 reference units with a separate P-frame. 80% of time reductions, compared with the With this criterion, a wide range of textures and content is covered, where the chosen sequences are: Campfire (Class A), BasketballDrive (Class B), BQMall (Class C), BQSquare (Class D) and KristenAndSara (Class E). FFmpeg [2] was used to evaluate the performance of H. We also provide the scrips for generating all the RD curves of our paper in folder RDCurve. This repository contains the HEVC dataset comprising of 1 second and 2 second long segments (suitable for low latency applications) as well as the tool to generate HEVC encoded segments using CRF a 2020. 265 on the HEVC Class B and Class C datasets. 266 reference software CANF-VC++ achieves significant Bj{\o}ntegaard-Delta rate savings on conventional datasets UVG, HEVC Class B and MCL-JCV, outperforming the baseline CANF-VC and even the H. json. 2020. from publication: Boosting neural video codecs by exploiting hierarchical redundancy | In Datasets such as HEVC Class A-E [1], UVG [2], and MCL-JCV [3] feature 4K and HD video sequences with high spatial and temporal complexity which is useful for assessing codec performance in entertain-ment applications. The bpp and MS-SSIM values are averaged on each class. 7% rate Experimental results show the proposed framework outperforms the SOTA deep neural video compression networks on the HEVC-class B dataset and is competitive on the UVG and MCL-JCV datasets. 7% The evaluation on the HEVC Class B dataset reveals that incorporating the RCA into the DRA framework results in a marginal increase in coding time—merely around 0. 4 Total bitrate (Bpp) (a) 0. For example, one video of HEVC Class B can be prepared as: Crop the original YUV via ffmpeg: The BD-rate values are computed under PSNR on HEVC class B dataset. For each video frame, ground truth annotations include object class ID, object ID, and bounding box location and its dimensions. 95%, 15. However, with the aim of homogenizing the number of instances per class and avoiding overfitting to the higher resolution sequences, In addition, a hierarchical coding structure is also proposed in this paper. 264. 26% than x265 on UVG dataset. 4. 95%, 29. It consists of 15 video clips in four classes: B-E. Among them, our encoding time is the time from loading the video frame to encoding it into the binary bitstream. 05 s per frame, as shown in Fig. 9750 0. Download scientific diagram | Distributions of the maximum and minimum motion vector components along the horizontal (left) and vertical (right) axes of the HEVC Class B dataset. 89dB @0. 5. On the other hand, for the LB-Main profile, the proposed algorithm achieved, on average, 44. 9850 0. Compression Performance on the HEVC Class C and E Datasets We provide the compression results on the HEVC Class B and D datasets in the paper. /Plot. (a) and (b) represent two consecutive frames from the HEVC Class C dataset, (c) and (e) represent the raw optical flow and its flow magnitude, (d) and (f On the other hand, we also evaluate the human viewing quality of the encoded bitstream for machine, using “DivGop" encoding structure on HEVC Class B and Class C test datasets. As we are not making the content available and only the decoding parameters in the header files, we can 28 1. For example, one video of HEVC Class B can be prepared as: Crop the original YUV via ffmpeg: In addition, a hierarchical coding structure is also proposed in this paper. In addition, it generates high-quality reconstructed frames in comparison with conventional motion coding-based symmetric auto-encoder meanwhile its model Experimental results show the proposed framework outperforms the SOTA deep neural video compression networks on the HEVC-class B dataset and is competitive on the UVG and MCL-JCV datasets. py and rd_plot_HEVC_B. 264 and H. FR, N, Q, GoP represent the frame rate, the number of frames to be encoded, the quality and Image files: Each image may have different size, and is one frame extracted from a video. 265, DVC, refine (Our proposed method without online updating strategy), refine+ (the first optical flow frame is also refined by our network) and refine+OU (adding online updating strategy to “refine+”) Evaluation Dataset We validate our method on three 2K resolution benchmark datasets, UVG , MCL-JCV , and HEVC Class B . Jun 11, 2020 · 2. The UVG (Ultra Video Group) dataset consists of 7 videos, each containing 600 frames except for the one having 300 frames. The UVG dataset and the HEVC Standard Test Sequences (Class B, Class C, Class D, and Class E) are used to evaluate our model. 8, we also present the compression results on the HEVC Class C and E datasets using H. perceptually In this context, on average, Class A provided a smaller degree of time reduction than Class B due to the presence of the Nabuta sequence in Class A, which provided only a 25% of time reduction. 5 0. The priority of the codec is to reduce interpolated artifacts HEVC Class B dataset, the encoder learned from the training dataset is already. • and testing datasets. 3. For the video sequences with simple motion scenes, such as the HEVC Class B dataset, the encoder learned from the training dataset is already near-optimal and it only requires 3 iterations to obtain the optimal parameters. 5 32 RA RA w/o GFP LD LD w/o GFP RA (r=1) 31 30 0. The priority of the codec is to reduce interpolated artifacts by compensating for pixel-level detailed residual. 2. We use variants to distinguish between results evaluated on slightly different versions of the same dataset. 175 0. FVC: An End-to-End Framework Towards Deep Video Compression in Feature Space. DCVC can use fewer bits while achieving much better reconstruction quality. While the latest neural video compression work DCVC-DC [] has surpassed the performance of the best traditional video The performance gain compared with bi-directional prediction mainly comes from sequences that with large and complicated motions such as the Jockey sequence in UVG dataset, the BasketballDrive sequence in HEVC Class B dataset and the RaceHorsesC sequence in HEVC Class C dataset. In the supplemental material, you'll find the findings for HEVC Class C and Class D. 025 0. 0 as the anchor. Performance We provide a dataset of object labels for raw (uncompressed) HEVC v1 CTC video sequences. Each reference unit has 3 frames. 125 0. cs. For testing, we follow [18, 38, 62] and employ the 1080p datasets UVG , MCL-JCV and HEVC Class B . 266 of UVG dataset and HEVC Class B dataset on MS-SSIM. 9k次。巧妇难为无米之炊，身为一个码农怎能碗里没有米呢？想必很多朋友都碰到下载测试序列的困惑，为了减少麻烦，现提供HEVC所有测试序列的下载，我已经上传到360网盘上，方便大家下载。主要的测试序列如下：Test sequencesClassSequence nameFrame countFram_hevc class b、c、d和e数据集在 Class B is five 1080p natural video sequences, Class C is four WVGA natural sequences, Class D is four WQVGA natural video sequences, and Class E is three 720p high-quality video sequences of people talking and 4 videos of screen content of various resolutions. Our work demonstrates the potential of integrating advancements in video compression and serves as inspiration for future research in the field. BD rate savings for RGB PSNR with with respect to the SSF base model. from publication CANF-VC++ achieves significant Bj{\o}ntegaard-Delta rate savings on conventional datasets UVG, HEVC Class B and MCL-JCV, outperforming the baseline CANF-VC and even the H. 9825 0. 266 codec at higher bitrates as assessed using the perceptually relevant MS-SSIM algorithm. Download scientific diagram | (a) Ablation study of adaptive DCT loss on UVG dataset (b) Ablation study of hyper-parameter α on HEVC Class B and MCL JCV dataset from publication: Rate-Perception video is BasketballDrive from HEVC Class B dataset. The Joint Collaborative Team on 3D Video Coding Extension Development (JCT-3V) was established to work on multiview and 3D video coding extensions of HEVC and other video coding Jun 13, 2024 · In recent years, deep learning-based neural video compression (NVC) schemes have achieved great success. 6/SG 16) and ISO/IEC Moving Picture Experts Group (ISO/IEC JTC 1/SC 29/WG 11). 文章浏览阅读1. From this example, we can find that our DCVC can efficiently alleviate the error-propagation problem. While the latest neural video compression work DCVC-DC [] has surpassed the performance of the best traditional video compression 2. 075 0. These results show that our network outperformed both H. Experiments Results. 9k次。巧妇难为无米之炊，身为一个码农怎能碗里没有米呢？想必很多朋友都碰到下载测试序列的困惑，为了减少麻烦，现提供HEVC所有测试序列的下载，我已经上传到360网盘上，方便大家下载。主要的测试序列如下：Test sequencesClassSequence nameFrame countFram_hevc class b、c、d和e数据集在 video is BasketballDrive from HEVC Class B dataset. For this reason, we conduct the experiments under larger This page presents a Ultra High Definition HEVC DASH dataset ranging from HD to UHD in different bit rates, tempral resolution and bit depth. Between the other two methods, our method preserves more textures. Further details on this dataset are available in this article. Our trace-based simulation dataset provides a means of evaluation in frameworks such as NS-2 and NS-3, while our testbed evaluation dataset offers a means of analysing the delivery of content over a We present a dataset called SFU-HW-Objects-v1, which contains bounding boxes and object class labels for High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) video sequences [1], [2]. In Figure2, we also report the performance of our model when using the Kinetics dataset as the train dataset, which is denoted as Ours Kinetics (see the CANF-VC++ achieves significant Bj{\o}ntegaard-Delta rate savings on conventional datasets UVG, HEVC Class B and MCL-JCV, outperforming the baseline CANF-VC and even the H. We use an enhanced version of DVC [ 28 ] called “ \(\hbox {DVC}^{*}\) ” as our baseline method, where the entropy models of both motion vector (MV) feature and residual feature are modeled by the mean-scale hyperprior. Network performance on HEVC Class B, C, D and Ewap hotel datasets. fi) under a non For performance evaluation, we use the video sequences from the HEVC Class B and Class C datasets. Full size table. The result in Fig. Experimental results show the proposed framework outperforms the SOTA deep neural video compression networks on the HEVC-class B dataset and is competitive on the UVG and MCL-JCV datasets. 1. 11%, and 23. In addition, it generates high-quality re- constructed frames in comparison with We take the HEVC Class B dataset (1080p) to compare the encoding and decoding speed. 265 . 1 Network Parameters To search for suitable parameters of coding frame number N and the down-scale times M with max-pooling, control experiments are implemented. The UVG dataset contains seven high frame rate videos with the resolution of 1920 × 1080 1920 1080 1920\times 1080 , in which the difference between the neighboring frames is small. 100 0. The images are cropped to 1920 × 1024 1920 1024 1920\times 1024 1920 × 1024 by center cropping to ensure that the input image shape is divisible by 128. The representation rates and resolutions of our dataset range from 40Mbps in 4K down to 235kbps in 320x240, and are comparable to rates utilised by on We present a dataset that contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. The presented dataset contains only object labels; video sequences themselves can be obtained from the Joint Collaborative Team on Video Coding Consequently, it improves the visual quality of the reconstructed frames. Take the class B in the HEVC test sequence as an example, you need to crop 1920x1080 to resolution videos (UVG dataset, HEVC Class B dataset, and. 266 codec at higher bitrates as assessed using the. DCVC can use fewer bits while distortion curves for H. Precisely, 3 I For video compression, we use Vimeo-90k [51] dataset, which has 89,800 video clips with the resolution of 256 × 256, as our training dataset, and evaluate our model on the HEVC Standard Test Download scientific diagram | Network performance on HEVC Class B, C, D and Ewap hotel datasets. comparable performance to x265 at both high rates and low. 032 Bpp. Figure 2: An example from BasketballDrive in HEVC Class B dataset. The presented dataset contains only object labels; raw video sequences themselves can be obtained from the Joint Collaborative Team on Video Coding (JCT-VC). Evaluation results on the UVG dataset and HEVC Class B (1080p) and Class E (720p). After testing, you will get the result for UVG and HEVC class B datasets. The RD performance for each method on HEVC Class B, HEVC Class C, HEVC Class D, and UVG datasets. The dataset is published on our website (ultravideo. 5BPP H. 462BPP h (b) Frames with fast moving objects Fig. has been trained to Figure 2: An example from BasketballDrive in HEVC Class B dataset. 9800 0. 49% bitrate savings and 0. Typical deep neural video compression networks usually follow the hybrid approach of classical video coding that contains two separate modules: motion coding and In Fig. 5w次，点赞21次，收藏38次。高清YUV序列下载地址点这儿打开网页往下拉，上面的是比较古老的序列，下面有各种高清的序列，在学校网络限制流量，等回家了之后下载下来，看看能不能转到网盘里面。HEVC序列下载：巧妇难为 PSNR of HEVC test sequences for different compression codecs. This demonstrates that the parameter reuse strategy effectively unleashes the parameter Download scientific diagram | RGB rate-distortion curves for the UVG, HEVC Class-B, and MCL-JCV datasets. These experiments are performed on three video sequences from the HEVC Class B dataset (Kimono1, ParkScene, and Cactus). 264) and HEVC (H. The scope of this group was To verify the performance of intra video compression, proposed model is also evaluated on the HEVC Class B dataset since video is the merging of each frame. The HEVC datasets contain 22 The UVG dataset with higher frame rate (120 fps) is less affected by motion alignment than the HEVC Class B dataset, which may result from its high similarity between adjacent frames. 264 (veryslow) 0. 266 reference software VTM. These are referred to as “FVC DivGoP" and “TCM DivGoP" in Fig. 265) video content, generated from three well known open-source 4K video clips. 6 or higher) FFmpeg; Test dataset. For this reason, we conduct the experiments under larger We also utilize the 19 video sequences of HEVC datasets [38] including Class B (1920 × 1080), Class C (832 × 480), Class D (416 × 240), Class E (1280 × 720) and Class E ′ (1280 × 720). The results are evaluated for the UVG dataset. However, they do not capture the specific characteristics of real-world video conferencing streams. The dataset is used in the In this part, we will introduce the experiments. These datasets provide a wide BDBR results (%) when compared with H. 265 on the HEVC Class C and Class D datasets in Figure1. 1 Introduction With the increasing amount of video content, it is a huge challenge to store and transmit videos. comparison between standard In recent years, deep learning-based neural video compression (NVC) schemes have achieved great success. In addition, it generates high-quality reconstructed frames in comparison with conventional motion coding-based symmetric auto-encoder meanwhile its model A. It is suggested to change all the three q_scales together and generate the interpolated q_scales Datasets. F or the videos with complex motion PyTorch implementation and benchmark of Video Compression - PyTorchVideoCompression/README. The GoP size is set as 32. The third line is the VideoSRC14 sequence from MCL-JCV dataset compressed to about 0. . Consequently, it improves the visual quality of the reconstructed frames. 9700 0. 254 and H. Tests RGB rate-distortion curves for the UVG, HEVC Class-B, and MCL-JCV datasets. 8. Compared with PSNR, MS-SSIM is generally more related to perceptual quality, especially at low bit rates. 264, H. If you find our paper useful, please cite: RD curves of PSNR and MS-SSIM on UVG, MCL-JCV, and HEVC Class B datasets. Result Using Kinetics Dataset as the Train Dataset Wu et al. When compared with the flow coding method in DVC , our overall RaFC framework can compress motion information in a much more effective way and save up to 70% bits at the 文章浏览阅读1. For training, we adopt the methodology outlined in , utilizing the ready-made Vimeo-90k dataset and raw Vimeo videos . the latest H. Download: Download high-res image (768KB) Download: Download full-size image; Fig. rates, resulting in 1% overall rate reductions with the single-rate model and 7. JVET extended the HEVC test set to support H. Datasets such as HEVC Class A-E [], UVG [], and MCL-JCV [] feature 4K and HD video sequences with high spatial and temporal complexity which is useful for assessing codec performance in entertainment applications. tut. Requirement. components one by one in a row from the DeepPVCnet. After that, we compare the performance of our proposed method with related codecs on UVG , MCL-JCV , and HEVC Standard Test Sequences(Class B, Class C, Class D, and Class E) . 0 (AI default) . For AI configuration on Kodak dataset, I 2 VC exhibits fewer artifacts and achieves similar visual quality with a lower bit-rate compared to VTM-19. In addition, it generates high-quality re- constructed frames in comparison with methods with DeepSVC on HEVC Class B dataset. Our solution clearly surpasses previous video compression research by a wide margin. For each video frame, ground truth annotations include object class ID, We evaluate our model using UVG , HEVC Class B, and MCL-JCV datasets, which both have a resolution of 1920 × 1080 1920 1080 1920\times 1080 1920 × 1080 (1080P). We configure all the tests under low-delay encoding settings. 266/VVC [6] The HEVC datasets contain 16 videos in Class B, C, D and E with different resolutions from 416 × 240 416 240 416\times 240 to 1920 × 1080 1920 1080 1920\times 1080. md at master · ZhihaoHu/PyTorchVideoCompression HEVC Class C and Class D datasets, respectively. CANF-VC++ achieves significant Bjøntegaard-Delta rate savings on conventional datasets UVG, HEVC Class B and MCL-JCV, outperforming the baseline CANF-VC and even the H. py, in . 96% with the HEVC-class B dataset, and outperforms We employ the Vimeo-90K dataset for training, and the HEVC Class B , UVG , and MCL-JCV datasets for inference. 91dB @0. 150 0. • Our dataset can benefit the research at the intersection of video coding and computer vision. Specifically, we designed a new coding mode including three types of frame: I-frame, P-frame, and cI-frame, to reduce the bit rate of I-frame PSNR of HEVC test sequences for different compression codecs. The texture on the stone pillar is blurred in the frame compressed with VTM (LD). 6% bitrate save on HEVC Class D dataset when compares with our base single-reference frame It is recommended that the --worker number is equal to your GPU number. It is crucial to test video codecs with and testing datasets. Our decoding time is the time from loading the bitstream High Efficiency Video Coding (HEVC) is currently being prepared as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. To the authors' best knowledge, this is the first Test dataset. Dec 23, 2016 · 文章浏览阅读1. This repository contains the HEVC dataset comprising of 1 second and 2 second long segments (suitable for low latency applications) as well as the tool to generate HEVC encoded segments using CRF as the rate control mode. utilized the Kinetics datasets to train the video compression mdoel in [2]. 264 and the HEVC standard against MS-SSIM. We test 96 frames for each video sequence. Please fill the result of UVG and HEVC class B datasets in rd_plot_UVG. Jan 25, 2023 · distortion curves for H. This paper proposed a high-performance and efficient learned video compression approach named AlphaVC. Download scientific diagram | Detailed rate-distortion performance of B-EPIC(MSE) on the HEVC class-B dataset. 4b), our models show. All the competing methods use ANFIC [3] as the intra-frame coder and are evaluated under the same setting, namely, 96-frame encoding with GOP size 32. 030 Bpp. 264 (veryslow) Fig. 149BPP h (a) Frames with slow moving objects. Paper title: * Dataset or its variant: * Task: * Model name This data set may be used to simulate UHD DASH services, whether on-demand or live, using real-life professional quality content, and is presented ranging from HD to UHD in different bit rates. The experimental findings on the UVG dataset, the HEVC Class B and Class E datasets are shown in Fig. from publication: Lightweight Hybrid Video Compression Framework Using Reference-Guided On the other hand, in the HEVC Class B dataset, regarding PSNR, it exhibits state-of-the-art results; nevertheless, in MS-SSIM, it has roughly an 18% gap compared to the FVC-CVPR21 [35]. py, HEVC Class B dataset (Fig. 1. Fig. 265, DVC, refine (Our proposed method without online updating Despite the key differences between video conferencing and entertainment video, current benchmarking datasets remain focused on the latter. The first neural video compression work DVC [] just slightly exceeded the traditional video compression standard H. The new dataset is called SFU-HW-Tracks-v1 and contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. 02: Upload Benchmark of the HEVC, UVG, MCL-JCV, VTL datasets. 2 (b) 0. A2: Comparison of rate-distortion curves on UVG, HEVC Class B, and MCL-JCV datasets for both PSNR and MS-SSIM. The Joint Collaborative Team on Video Coding (JCT-VC) was established to work on this project. 7 . You can also specify different q_scales values to test other bitrate points. video is BasketballDrive from HEVC Class B dataset. 265 27. 3 0. 文章浏览阅读2. 04% bitrate on HEVC Class B, Class C, Class D, Class E, respectively, but consumes more bitrate by 7. For the videos with complex motion scenes, such as the HEVC Class C dataset, more iterations are required to learn the 2) Testing Dataset: To evaluate the performance of our DCVC-B scheme, we use the video sequences from HEVC dataset [57], UVG dataset [58], and MCL-JCV dataset [59]. swwf nrcrf eoxte sicue lmpnun dtghzk tdb cdqsk lvhjmg uiw