Fast MPEG-CDVS Encoder With GPU-CPU Hybrid Computing

Duan, Ling-Yu; Sun, Wei; Zhang, Xinfeng; Wang, Shiqi; Chen, Jie; Yin, Jianxiong; See, Simon; Huang, Tiejun; Kot, Alex C.; Gao, Wen

doi:10.1109/tip.2018.2794203

Cited by 14 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CDVA further extends the concept CDVS by including deep features alongside the handcrafted ones and exploiting temporal redundancies between video frames. We found only three real-time implementations of CDVS [43,51,133] reaching 98, 144 and 39 megapixels per second on desktop-grade GPUs, respectively. Compared to other implementations reported later in the survey, the results are relatively slow, and more research would be necessary to decrease the computational complexity of CDVS and CDVA.…”

Section: The Role Of Machine Learningmentioning

confidence: 90%

Image and Video Coding Techniques for Ultra-low Latency

et al. 2022

View full text Add to dashboard Cite

The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.

show abstract

Section: The Role Of Machine Learningmentioning

confidence: 90%

Image and Video Coding Techniques for Ultra-low Latency

et al. 2022

View full text Add to dashboard Cite

show abstract

“…General purpose graphical processor units (GPGPU) are becoming more and more invasive in every-day life [13][14][15][16][17][18].…”

Section: Gpgpu and Parallel Programming Basic Notionsmentioning

confidence: 99%

A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

et al. 2019

View full text Add to dashboard Cite

Research on autonomous cars, early intensified in the 1990s, is becoming one of the main research paths in automotive industry. Recent works use Rapidly-exploring Random Trees to explore the state space along a given reference path, and to compute the minimum time collision-free path in real time. Those methods do not require good approximations of the reference path, they are able to cope with discontinuous routes, they are capable of navigating in realistic traffic scenarios, and they derive their power from an extensive computational effort directed to improve the quality of the trajectory from step to step. In this paper, we focus on re-engineering an existing state-of-the-art sequential algorithm to obtain a CUDA-based GPGPU (General Purpose Graphics Processing Units) implementation. To do that, we show how to partition the original algorithm among several working threads running on the GPU, how to propagate information among threads, and how to synchronize those threads. We also give detailed evidence on how to organize memory transfers between the CPU and the GPU (and among different CUDA kernels) such that planning times are optimized and the available memory is not exceeded while storing massive amounts of fuse data. To sum up, in our application the GPU is used for all main operations, the entire application is developed in the CUDA language, and specific attention is paid to concurrency, synchronization, and data communication. We run experiments on several real scenarios, comparing the GPU implementation with the CPU one in terms of the quality of the generated paths and in terms of computation (wall-clock) times. The results of our experiments show that embedded GPUs can be used as an enabler for real-time applications of computationally expensive planning approaches.

show abstract

“…Even if we improve the communication performance of the network, the lossy compression part will become the bottleneck to transfer the video stream to the abnormal detection part due to the heavy calculation of the compression processes. To overcome the difficulty of improving the lossy compression performance, we can accelerate the compressor and the decompressor by applying GPUs [ 15 ] and dedicated hardware [ 16 ]. However, the lossy compression reduces information of pixels in frames and guarantees the bandwidth of the video stream.…”

Section: Introductionmentioning

confidence: 99%

Stream-Based Visually Lossless Data Compression Applying Variable Bit-Length ADPCM Encoding

Yamagiwa

Ichinomiya

2021

Sensors

View full text Add to dashboard Cite

Video applications have become one of the major services in the engineering field, which are implemented by server–client systems connected via the Internet, broadcasting services for mobile devices such as smartphones and surveillance cameras for security. Recently, the majority of video encoding mechanisms to reduce the data rate are mainly lossy compression methods such as the MPEG format. However, when we consider special needs for high-speed communication such as display applications and object detection ones with high accuracy from the video stream, we need to address the encoding mechanism without any loss of pixel information, called visually lossless compression. This paper focuses on the Adaptive Differential Pulse Code Modulation (ADPCM) that encodes a data stream into a constant bit length per data element. However, the conventional ADPCM does not have any mechanism to control dynamically the encoding bit length. We propose a novel ADPCM that provides a mechanism with a variable bit-length control, called ADPCM-VBL, for the encoding/decoding mechanism. Furthermore, since we expect that the encoded data from ADPCM maintains low entropy, we expect to reduce the amount of data by applying a lossless data compression. Applying ADPCM-VBL and a lossless data compression, this paper proposes a video transfer system that controls throughput autonomously in the communication data path. Through evaluations focusing on the aspects of the encoding performance and the image quality, we confirm that the proposed mechanisms effectively work on the applications that needs visually lossless compression by encoding video stream in low latency.

show abstract

Fast MPEG-CDVS Encoder With GPU-CPU Hybrid Computing

Cited by 14 publications

References 35 publications

Image and Video Coding Techniques for Ultra-low Latency

Image and Video Coding Techniques for Ultra-low Latency

A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

Stream-Based Visually Lossless Data Compression Applying Variable Bit-Length ADPCM Encoding

Contact Info

Product

Resources

About