Ultra-low-latency Video Coding Method for Autonomous Vehicles and Virtual Reality Devices

Author(s):  
Seiji Mochizuki ◽  
Kousuke Imamura ◽  
Kaito Mori ◽  
Yoshio Matsuda ◽  
Tetsuya Matsumura
Author(s):  
Yogananda Patnaik ◽  
Dipti Patra

Video coding is an imperative part of the modern day communication system. Furthermore, it has vital roles in the fields of video streaming, multimedia, video conferencing and much more. Scalable Video Coding (SVC) is an emerging research area, due to its extensive application in most of the multimedia devices as well as public demand. The proposed coding technique is capable of eliminating the Spatio-temporal regularity of a video sequence. In Discrete Bandelet Transform (DBT), the directions are modeled by a three-directional vector field, known as structural flow. Regularity is decided by this flow where the data entropy is low. The wavelet vector decomposition of geometrically ordered data results in a lesser extent of significant coefficients. The directions of geometrical regularity are interpreted with a two-dimensional vector, and the approximation of these directions is found with spline functions. This paper deals with a novel SVC technique by exploiting the DBT. The bandelet coefficients are further encoded by utilizing Set Partitioning in Hierarchical Trees (SPIHT) encoder, followed by global thresholding mechanism. The proposed method is verified with several benchmark datasets using the performance measures which gives enhanced performance. Thus, the experimental results bring out the superiority of the proposed technique over the state-of-arts.


2021 ◽  
Author(s):  
Shiva Raj Pokhrel ◽  
Neeraj Kumar ◽  
Anwar Walid

Connected Autonomous Vehicles (CAVs) are Not-So-Futuristic. CAVs will be highly dynamic by intelligently exploiting multipath communication over several radio technologies, such as high-speed WiFi and 5G and beyond networks. Yet, the likelihood of data communication loss can be very high and/, or packets arrive at the destination not in correct working order due to erratic and mixed time-varying wireless links. Furthermore, the vehicular data traffic is susceptible to loss and delay variation,which recommends the need to investigate new multipath TCP(MPTCP) protocols for ultra-reliable low latency communication(URLLC) over such heterogeneous networks while reassuring CAVs’ needs. We undertake the challenge by jointly considering network coding and balanced linked adaptation for performing coupled congestion control across multiple wireless paths.Consequently, the proposed low delay MPTCP framework for connecting autonomous vehicles is efficient and intelligent by design. We conduct a rigorous convergence analysis of the MPTCP design framework. In summation, we provide a detailed mathematical study and demonstrate that the latency penalty for the URLLC-MPTCP developed over these networks becomes negligible when considering the possible benefits that multiple network convergence could offer. Our extensive emulation results demonstrate all these lucrative features of URLLC-MPTCP.


IEEE Network ◽  
2018 ◽  
Vol 32 (2) ◽  
pp. 78-84 ◽  
Author(s):  
Mohammed S. Elbamby ◽  
Cristina Perfecto ◽  
Mehdi Bennis ◽  
Klaus Doppler
Keyword(s):  

Author(s):  
Youngjo Kim ◽  
Juwon Byun ◽  
Seulgi Yang ◽  
Jaeseok Kim
Keyword(s):  

Author(s):  
JUNMEI ZHONG ◽  
C. H. LEUNG ◽  
Y. Y. TANG

In recent years, wavelets have attracted great attention in both still image compression and video coding, and several novel wavelet-based image compression algorithms have been developed so far, one of which is Shapiro's embedded zerotree wavelet (EZW) image compression algorithm. However, there are still some deficiencies in this algorithm. In this paper, after the analysis of the deficiency in EZW, a new algorithm based on quantized coefficient partitioning using morphological operation is proposed. Instead of encoding the coefficients in each subband line-by-line, regions in which most of the quantized coefficients are significant are extracted by morphological dilation and encoded first. This is followed by using zerotrees to encode the remaining space which has mostly zeros. Experimental results show that the proposed algorithm is not only superior to the EZW, but also compares favorably with the most efficient wavelet-based image compression algorithms reported so far.


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