Video Slice: Image Compression and Transmission for Agricultural Systems

When agricultural automation systems are required to send cultivation field images to the cloud for field monitoring, pay-as-you-go mobile communication leads to high operation costs. To minimize cost, one can exploit a characteristic of cultivation field images wherein the landscape does not change considerably besides the appearance of the plants. Therefore, this paper presents a method that transmits only the difference data between the past and current images to minimize the amount of transmitted data. This method is easy to implement because the difference data are generated using an existing video encoder. Further, the difference data are generated based on an image at a specific time instead of the images at adjacent times, and thus the subsequent images can be reproduced even if the previous difference data are lost because of unstable mobile communication. A prototype of the proposed method was implemented with a MPEG-4 Visual video encoder. The amount of transmitted and received data on the medium access control layer was decreased to approximately 1/4 of that when using the secure copy protocol. The transmission time for one image was 5.6 s; thus, the proposed method achieved a reasonable processing time and a reduction of transmitted data.

Machine learning-based energy consumption modeling and comparison of H.264/AVC and google VP8 encoders

Advancement of the prediction models used in a variety of fields is a result of the contribution of machine learning approaches. Utilizing such modeling in feature engineering is exceptionally imperative and required. In this research, we show how to utilize machine learning to save time in research experiments, where we save more than five thousand hours of measuring the energy consumption of encoding recordings. Since measuring the energy consumption has got to be done by humans and since we require more than eleven thousand experiments to cover all the combinations of video sequences, video bit_rate, and video encoding settings, we utilize machine learning to model the energy consumption utilizing linear regression. VP8 codec has been offered by Google as an open video encoder in an effort to replace the popular MPEG-4 Part 10, known as H.264/AVC video encoder standard. This research model energy consumption and describes the major differences between H.264/AVC and VP8 encoders in terms of energy consumption and performance through experiments that are based on machine learning modeling. Twenty-nine raw video sequences are used, offering a wide range of resolutions and contents, with the frame sizes ranging from QCIF(176x144) to 2160p(3840x2160). For fairness in comparison analysis, we use seven settings in VP8 encoder and fifteen types of tuning in H.264/AVC. The settings cover various video qualities. The performance metrics include video qualities, encoding time, and encoding energy consumption.

The Influence of Coding Tools on Immersive Video Coding

This paper summarizes the research on the influence of HEVC (High Efficiency Video Coding) configuration on immersive video coding. The research was focused on the newest MPEG standard for immersive video compression – MIV (MPEG Immersive Video). The MIV standard is used as a preprocessing step before the typical video compression thus is agnostic to the video codec. Uncommon characteristics of videos produced by MIV causes, that the typical configuration of the video encoder (optimized for compression of natural sequences) is not optimal for such content. The experimental results prove, that the performance of video compression for immersive video can be significantly increased when selected coding tools are being used.

Hardware design of a scalable and fast 2-D hadamard transform for HEVC video encoder

<span>This paper presents the hardware design of a 2-dimensional Hadamard transform used the in the rate distortion optimization module in state-of-the-art HEVC video encoder. The transform is mainly used to quickly determine optimum block size for encoding part of a video frame. The proposed design is both scalable and fast by 1) implementing a unified architecture for sizes 4x4 to 32x32, and 2) pipelining and feed through control that allows high performance for all block sizes. The design starts with high-level algorithmic loop unrolling optimization to determine suitable level of parallelism. Based on this, a suitable hardware architecture is devised using transpose memory buffer as pipeline memory for maximum performance. The design is synthesized and implemented on Xilinx Kintex Ultrascale FPGA. Results indicate variable performance obtained for different block sizes and higher operating frequency compared to a similar work in literature. The proposed design can be used as a hardware accelerator to speed up the rate distortion optimization operation in HEVC video encoders.</span>