Down-Sampling Based Rate Control for Mobile Screen Video Coding

Tang, Tong; Yang, Jiankai; Du, Biao; Tang, Liuwei

doi:10.1109/access.2019.2943887

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Especially with the development of Internet of Things [3][4][5][6] and 5G techniques [7,8], the transmission of video and multimedia information in mobile communication have become the current hot technology, and improving the compression performance of mobile videos could combine the mobile application with communication better in VSNs. Due to the increasing pressure of video storage and transmission [9,10], more and more efficient video coding standards have been put out in the last few decades. High-Efficiency Video Coding (HEVC/H.265) [11] is developed by Joint Collaborative Team of Video Coding (JCT-VC).…”

Section: Introductionmentioning

confidence: 99%

Fast Sample Adaptive Offset Jointly Based on HOG Features and Depth Information for VVC in Visual Sensor Networks

Wang

Tang

2020

Sensors

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Visual sensor networks (VSNs) can be widely used in multimedia, security monitoring, network camera, industrial detection, and other fields. However, with the development of new communication technology and the increase of the number of camera nodes in VSN, transmitting and compressing the huge amounts of video and image data generated by video and image sensors has become a major challenge. The next-generation video coding standard—versatile video coding (VVC), can effectively compress the visual data, but the higher compression rate is at the cost of heavy computational complexity. Therefore, it is vital to reduce the coding complexity for the VVC encoder to be used in VSNs. In this paper, we propose a sample adaptive offset (SAO) acceleration method by jointly considering the histogram of oriented gradient (HOG) features and the depth information for VVC, which reduces the computational complexity in VSNs. Specifically, first, the offset mode selection (select band offset (BO) mode or edge offset (EO) mode) is simplified by utilizing the partition depth of coding tree unit (CTU). Then, for EO mode, the directional pattern selection is simplified by using HOG features and support vector machine (SVM). Finally, experimental results show that the proposed method averagely saves 67.79% of SAO encoding time only with 0.52% BD-rate degradation compared to the state-of-the-art method in VVC reference software (VTM 5.0) for VSNs.

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Section: Introductionmentioning

confidence: 99%

Fast Sample Adaptive Offset Jointly Based on HOG Features and Depth Information for VVC in Visual Sensor Networks

Wang

Tang

2020

Sensors

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“…One of the traditional methods for the flow controls uses a threshold of FIFO buffer of the video frames in the producer side. In addition, we can find that one of the typical controls for the data rate is a up/down sampling of video frames [20]. It is controlled by a sampling parameter.…”

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confidence: 99%

Exception Handling Method Based on Event from Look-Up Table Applying Stream-Based Lossless Data Compression

2021

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It is getting popular to implement an environment where communications are performed remotely among IoT edge devices, such as sensory devices and the cloud servers due to applying, for example, artificial intelligence algorithms to the system. In such situations that handle big data, lossless data compression is one of the solutions to reduce the big data. In particular, the stream-based data compression technology is focused on such systems to compress infinitely continuous data stream with very small delay. However, during the continuous data compression process, it is not able to insert an exception code among the compressed data without any additional mechanisms, such as data framing and the packeting technique, as used in networking technologies. The exception code indicates configurations for the compressor/decompressor and/or its peripheral logics. Then, it is used in real time for the configuration of parameters against those components. To implement the exception code, data compression algorithm must include a mechanism to distinguish original data before compression and the exception code clearly. However, the conventional algorithms do not include such mechanism. This paper proposes novel methods to implement the exception code in data compression that uses look-up table, called the exception symbol. Additionally, we describe implementation details of the method by applying it to algorithms of stream-based data compression. Because some of the proposed mechanisms need to reserve entries in the table, we also discuss the effect against data compression performance according to experimental evaluations.

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A content-based intra rate-distortion model for HEVC-SCC

Esmaeeli

Rezaei

2022

Multimed Tools Appl

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Down-Sampling Based Rate Control for Mobile Screen Video Coding

Cited by 4 publications

References 38 publications

Fast Sample Adaptive Offset Jointly Based on HOG Features and Depth Information for VVC in Visual Sensor Networks

Fast Sample Adaptive Offset Jointly Based on HOG Features and Depth Information for VVC in Visual Sensor Networks

Exception Handling Method Based on Event from Look-Up Table Applying Stream-Based Lossless Data Compression

A content-based intra rate-distortion model for HEVC-SCC

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