This work focuses on exploiting the constructive interference among different users' data waveforms to introduce new coding and decoding techniques, which are specifically designed for nonorthogonal multiple acces...
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This work focuses on exploiting the constructive interference among different users' data waveforms to introduce new coding and decoding techniques, which are specifically designed for nonorthogonal multiple access (NOMA) systems. In this article, a structured coding scheme is devised. In essence, the proposed technique focuses on finding a relationship between the sent users' data waveforms and then uses this relationship in the decoding process at the receiving destination. It is worth pointing out that the proposed coding and decoding techniques exhibit better performance and reduced the complexity compared with the conventional uncoded NOMA. The complexity order evaluation shows that the proposed scheme attains a reduction in the required number of the floating point operations of 5 and 6 N at the second and third users, respectively, compared with that of the uncoded NOMA. Moreover, we have derived a closed-form expression for the bit error rate, which is verified using the Monte Carlo simulation. To demonstrate the practicality of the proposed system, the obtained results are compared with those of the uncoded and convolutional coding NOMA systems. Finally, the performance of the proposed system outperformed the conventional systems by an average of 5 dB in the case of two users and an average of 15 dB in the case of three users in the same work environment.
In this paper, a low-complexity coding approach is explored for depth video under the framework of high efficiency video coding based 3-D video coding standard. Unlike the existing low-complexity coding approaches tha...
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In this paper, a low-complexity coding approach is explored for depth video under the framework of high efficiency video coding based 3-D video coding standard. Unlike the existing low-complexity coding approaches that optimize motion search and mode decision, the major technical innovation of this paper is to incorporate depth sensitivity fidelity (DSF) into the rate-distortion optimization (RDO) process. Specifically, a quantitative maximum tolerable depth distortion is derived to measure the DSF, and jointly estimate the view synthesis distortion to account for the DSF. Then, a DSF-aware RDO scheme is proposed by developing new quantization parameter and Lagrangian multiplier determination strategies. Extensive experimental results demonstrate that the proposed method can reduce the computational complexity of encoding without significant view synthesis performance loss.
During and following the global COVID-19 pandemic, the use of screen content coding applications such as large-scale cloud office, online teaching, and teleconferencing has surged. The vast amount of online data gener...
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During and following the global COVID-19 pandemic, the use of screen content coding applications such as large-scale cloud office, online teaching, and teleconferencing has surged. The vast amount of online data generated by these applications, especially online teaching, has become a vital source of Internet video traffic. Consequently, there is an urgent need for low-complexity online teaching screen content (OTSC) coding techniques. Energy-efficient low-complexity green coding techniques for OTSC, named GCOTSC, are proposed based on the unique characteristics of OTSC. In the inter-frame prediction mode, the input frames are first divided into visually constant frames (VCFs) and non-VCFs using a VCF identifier. A new VCF mode has been proposed to code VCFs efficiently. In the intra-frame prediction mode, a heuristic multi-type least probable option skip mode based on static and dynamic historical information is proposed. Compared with the AVS3 screen content coding algorithm, using the typical online teaching screen content and AVS3 SCC common test condition, the experimental results show that the GOTSC achieves an average 59.06% reduction of encodingcomplexity in low delay configuration, with almost no impact on coding efficiency.
作者：
Shen, LiquanFeng, GuoruiAn, PingShanghai Univ
Shanghai Inst Adv Commun & Data Sci Shanghai 200072 Peoples R China Shanghai Univ
Key Lab Adv Display & Syst Applicat Shanghai 200072 Peoples R China Shanghai Univ
Sch Commun & Informat Engn Key Lab Specialty Fiber Opt Access Networ Shanghai 200072 Peoples R China Shanghai Univ
Sch Commun & Informat Engn Shanghai 200072 Peoples R China
The development of multimedia and hardware technologies has led to a great number of industrial video applications, such as virtual reality, high-definition video surveillance, and remote monitoring. As complex commun...
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The development of multimedia and hardware technologies has led to a great number of industrial video applications, such as virtual reality, high-definition video surveillance, and remote monitoring. As complex communication environments and heterogeneous networks are common in industrial applications, industrial videos are required to support a diverse range of display resolutions and transmission channel capacities. Scalable high-efficiency video coding (SHVC) standards provide the tools to meet this requirement. However, it is highly computationally expensive. codingcomplexity has a great impact on SHVC performance in industrial applications. Many of these applications are sensitive to time delay and have limited power. Thus, improvements are required to ensure the practical usability of SHVC encoders. In SHVC encoders, intrainterprediction of variable coding unit (CU) sizes is independently performed for the base and enhancement layers (ELs). There are many interlayer similarities that can be exploited to speed up the procedure for EL coding. In this paper, we propose a feedforward neural network aided model for CU size and mode decisions for SHVC, which utilizes base layer coding information and the coding data of spatiotemporal neighboring CUs to decide which CU sizes or prediction modes can be bypassed for certain EL CUs. Two feedforward neural network based learning models are built for CU classification, which are introduced in the procedures for CU size and mode decisions, respectively. According to the analysis from a large number of video sequences, the representative features are directly extracted from the coding information of previously coded neighboring CUs to avoid computational overheads. After the training is finished, these two models are designed and integrated to build classifiers. Then, two online classification approaches are designed for the CU size and mode decision procedures to classify each CUs type. Finally, different candidate CU siz
Distributed arithmetic coding (DAC) is similar to syndrome coding, in the sense that message sequences sharing the same interval can be considered a coset of the space of the source sequences, and the codeword is the ...
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Distributed arithmetic coding (DAC) is similar to syndrome coding, in the sense that message sequences sharing the same interval can be considered a coset of the space of the source sequences, and the codeword is the index of the coset. In this paper, the minimum Hamming distance of cosets is studied and it is proved that such a distance is as small as one. By only allowing the sequences with a large Hamming distance to overlap in the same interval, an improved DAC scheme is proposed. Simulation results show that, for equiprobable memoryless sources, this approach outperforms DAC in terms of decoding error rate at the same coding cost. In addition, at small sequence length, the decoding error rate of the proposed scheme is lower than that of distributed source coding based on low-density parity-check codes for highly correlated sources.
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