The Human visual System (HVS), with its intricate sophistication, is capable of achieving ultra-compact information compression for visual signals. This remarkable ability is coupled with high generalization capabilit...
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The Human visual System (HVS), with its intricate sophistication, is capable of achieving ultra-compact information compression for visual signals. This remarkable ability is coupled with high generalization capability and energy efficiency. By contrast, the state-of-the-art Versatile Video Coding (VVC) standard achieves a compression ratio of around 1,000 times for raw visualdata. This notable disparity motivates the research community to draw inspiration to effectively handle the immense volume of visualdata in a green way. Therefore, this paper provides a survey of how visualdata can be efficiently represented for green multimedia, in particular when the ultimate task is knowledge extraction instead of visual signal reconstruction. We introduce recent research efforts that promote green, sustainable, and efficient multimedia in this field. Moreover, we discuss how the deep understanding of the HVS can benefit the research community, and envision the development of future green multimedia technologies.
Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visualcompression towards competitive performance gains and ...
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ISBN:
(纸本)9798350349405;9798350349399
Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visualcompression towards competitive performance gains and diverse functionalities over traditional codecs. This paper provides a thorough review on the recent advances of generative visualcompression, illustrating great potentials and promising applications in ultra-low bitrate communication, user-specified reconstruction/filtering, and intelligent machine analysis. In particular, we review the visual data compression methodologies with deep generative models, and summarize how compact representation and high-quality reconstruction could be actualized via generative techniques. In addition, we generalize related generative compression technologies for machine vision with different-domain analysis. Finally, we discuss the fundamental challenges on generative visualcompression techniques and envision their future research directions.
作者：
Yamagiwa, ShinichiIchinomiya, YumaUniv Tsukuba
Fac Engn Informat & Syst 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan JST
PRESTO 4-1-8 Honcho Kawaguchi Saitama 3320012 Japan Univ Tsukuba
Dept Comp Sci 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan
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 smartphone...
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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 datacompression. Applying ADPCM-VBL and a lossless datacompression, 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.
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