Abstract
The final consumer of videos is mostly human. Therefore, if videos can be compressed by fully utilizing the perception characteristics of human visual systems (HVS), the bitrates of the compressed videos can be significantly reduced with subjective visual quality degradation as little as possible. Based on this, we newly propose a learning-based Just Noticeable Distortion (JND)-directed preprocessing scheme for perceptual video compression, especially for 10-bit High Dynamic Range (HDR) videos, which is called the HDR-JNDNet. Our HDR-JNDNet effectively suppresses the perceptual redundancy of 10-bit HDR video signals so that the compression efficiency can be significantly enhanced for the HEVC main10 profile encoder. To our best knowledge, our work is the first approach to training a CNN-based model to directly generate the JND-directed suppressed frames of 10-bit HDR video with the negligible perceptual quality difference between the decoded frames for the original HDR video input with and without the preprocessing by our HDR-JNDNet. Via intensive experiments, when the HDR-JNDNet is applied as preprocessing for the HDR video input before compression, it allows to remarkably save the required bitrates up to the maximum (average) 40.66% (18.37%) for 4K-UHD/HDR test videos, with little subjective video quality degradation without increasing the computational complexity.
Highlights
T ODAY, we live in the age of video
To find efficient High Dynamic Range (HDR) video transmission and saving solution, ISO/IEC JTC 1 (International Standard Organization/International Electrotechnical Commission Joint Technical Committee 1) had launched a Call for Evidence (CfE) and recommended an HDR video coding chain to be suitable for High Efficiency Video Coding (HEVC) [26] to process the HDR video in 10-bit integers quantized by Perceptual Quantizer (PQ) [8], [14]
In this paper, we firstly propose a learning-based preprocessing method based on Just Noticeable Distortion (JND)-directed energy-reduction for 10-bit HDR video signals, called HDR-JNDNet, which can significantly reduce perceptual redundancy of 4K-UHD/10bit HDR video for HEVC main10 encoder
Summary
T ODAY, we live in the age of video. It has become a daily life for everyone to shoot videos on their smartphones and share the captured videos via various internet video platforms. The required amounts of transmission bandwidths and storage spaces for such HDR video contents are much heavily demanded than the conventional SDR video contents because the HDR videos adopt floatingpoint representation [22]. To find efficient HDR video transmission and saving solution, ISO/IEC JTC 1 (International Standard Organization/International Electrotechnical Commission Joint Technical Committee 1) had launched a Call for Evidence (CfE) and recommended an HDR video coding chain to be suitable for High Efficiency Video Coding (HEVC) [26] to process the HDR video in 10-bit integers quantized by Perceptual Quantizer (PQ) [8], [14]. Note that the main profile of HEVC supports the format of encoded HDR video, and most of the HDR videos to be compressed are stored in the form of 10-bit quantized HDR. Quantized 10-bit HDR video contents still require more large amounts of transmission bandwidths and storage spaces than 8-bit SDR video contents. The 4K (3,840-pixel width) Ultra High Definition
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