R. James, Mohammed Abo-Zahhad, Koji Inoue, Mohammed S. Sayed
{"title":"嵌入式系统 Nvidia Jetson 平台上所有 Kvazaar 和 x265 HEVC 编码器的性能评估","authors":"R. James, Mohammed Abo-Zahhad, Koji Inoue, Mohammed S. Sayed","doi":"10.1007/s11554-024-01429-5","DOIUrl":null,"url":null,"abstract":"<p>The growing demand for high-quality video requires complex coding techniques that cost resource consumption and increase encoding time which represents a challenge for real-time processing on Embedded Systems. Kvazaar and x265 encoders are two efficient implementations of the High-Efficient Video Coding (HEVC) standard. In this paper, the performance of All Intra Kvazaar and x265 encoders on the Nvidia Jetson platform was evaluated using two coding configurations; highspeed preset and high-quality preset. In our work, we used two scenarios, first, the two encoders were run on the CPU, and based on the average encoding time Kvazaar proved to be 65.44% and 69.4% faster than x265 with 1.88% and 0.6% BD-rate improvement over x265 at high-speed and high-quality preset, respectively. In the second scenario, the two encoders were run on the GPU of the Nvidia Jetson, and the results show the average encoding time under each preset is reduced by half of the CPU-based scenario. In addition, Kvazaar is 54.5% and 56.70% faster with 1.93% and 0.45% BD-rate improvement over x265 at high-speed and high-quality preset, respectively. Regarding the scalability, the two encoders on the CPU are linearly scaled up to four threads and speed remains constant afterward. On the GPU, the two encoders are scaled linearly with the number of threads. The obtained results confirmed that, Kvazaar is more efficient and that it can be used on Embedded Systems for real-time video applications due to its high speed and performance over the x265 HEVC encoder</p>","PeriodicalId":51224,"journal":{"name":"Journal of Real-Time Image Processing","volume":"124 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance evaluation of all intra Kvazaar and x265 HEVC encoders on embedded system Nvidia Jetson platform\",\"authors\":\"R. James, Mohammed Abo-Zahhad, Koji Inoue, Mohammed S. Sayed\",\"doi\":\"10.1007/s11554-024-01429-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The growing demand for high-quality video requires complex coding techniques that cost resource consumption and increase encoding time which represents a challenge for real-time processing on Embedded Systems. Kvazaar and x265 encoders are two efficient implementations of the High-Efficient Video Coding (HEVC) standard. In this paper, the performance of All Intra Kvazaar and x265 encoders on the Nvidia Jetson platform was evaluated using two coding configurations; highspeed preset and high-quality preset. In our work, we used two scenarios, first, the two encoders were run on the CPU, and based on the average encoding time Kvazaar proved to be 65.44% and 69.4% faster than x265 with 1.88% and 0.6% BD-rate improvement over x265 at high-speed and high-quality preset, respectively. In the second scenario, the two encoders were run on the GPU of the Nvidia Jetson, and the results show the average encoding time under each preset is reduced by half of the CPU-based scenario. In addition, Kvazaar is 54.5% and 56.70% faster with 1.93% and 0.45% BD-rate improvement over x265 at high-speed and high-quality preset, respectively. Regarding the scalability, the two encoders on the CPU are linearly scaled up to four threads and speed remains constant afterward. On the GPU, the two encoders are scaled linearly with the number of threads. The obtained results confirmed that, Kvazaar is more efficient and that it can be used on Embedded Systems for real-time video applications due to its high speed and performance over the x265 HEVC encoder</p>\",\"PeriodicalId\":51224,\"journal\":{\"name\":\"Journal of Real-Time Image Processing\",\"volume\":\"124 1\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Real-Time Image Processing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11554-024-01429-5\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Real-Time Image Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11554-024-01429-5","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Performance evaluation of all intra Kvazaar and x265 HEVC encoders on embedded system Nvidia Jetson platform
The growing demand for high-quality video requires complex coding techniques that cost resource consumption and increase encoding time which represents a challenge for real-time processing on Embedded Systems. Kvazaar and x265 encoders are two efficient implementations of the High-Efficient Video Coding (HEVC) standard. In this paper, the performance of All Intra Kvazaar and x265 encoders on the Nvidia Jetson platform was evaluated using two coding configurations; highspeed preset and high-quality preset. In our work, we used two scenarios, first, the two encoders were run on the CPU, and based on the average encoding time Kvazaar proved to be 65.44% and 69.4% faster than x265 with 1.88% and 0.6% BD-rate improvement over x265 at high-speed and high-quality preset, respectively. In the second scenario, the two encoders were run on the GPU of the Nvidia Jetson, and the results show the average encoding time under each preset is reduced by half of the CPU-based scenario. In addition, Kvazaar is 54.5% and 56.70% faster with 1.93% and 0.45% BD-rate improvement over x265 at high-speed and high-quality preset, respectively. Regarding the scalability, the two encoders on the CPU are linearly scaled up to four threads and speed remains constant afterward. On the GPU, the two encoders are scaled linearly with the number of threads. The obtained results confirmed that, Kvazaar is more efficient and that it can be used on Embedded Systems for real-time video applications due to its high speed and performance over the x265 HEVC encoder
期刊介绍:
Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed.
Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application.
It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system.
The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.