In this paper, we propose a perceptual adaptive quantization based on a deep neural network on high efficiency video coding (HEVC) for bitrate reduction while maintaining subjective visual quality. The proposed algorithm adaptively determines frame-level QP values for different picture types of the hierarchical coding structure in HEVC by taking into account the high-level features extracted from the original and previously reconstructed pictures. A predefined model based on the visual geometry group (VGG-16) network is exploited to extract the high-level features for subjective visual characteristics. Furthermore, the Lagrange multiplier for each frame is also adaptively determined by involving the proposed features for deciding the appropriate parameter of the Lagrange multiplier that can be used for rate-distortion optimization during the encoding process. Experimental results reveal that the proposed perceptual adaptive QP selection can facilitate bitrate savings up to 65.73% and 47.68% and improve the BD-rate based on SSIM by approximately 20.68% and 14.27% under low-delay-P and random-access coding structures, respectively, with very minimal visual quality degradation when compared to HM-16.20 without adaptive QP selection. INDEX TERMS Adaptive quantization parameter, deep neural network, high efficiency video coding (HEVC), perceptual quantization parameter, VGG-16 network, video coding.
Abstract-Basically, a steganography indicates two of the principal requirements such the messages and the carrier file. Besides, it should have three aspects: capacity, imperceptibility, and robustness. This paper will show how to enhance the capacity of two types of carrier files for embedding message. By using Least Significant Bit method and modifying the four last bits of carrier files, bitmap and wav files could show the increasing of message size to be inserted to the carrier than only modifying the last 1 bit of carrier files. Particularly bitmap file which still had good quality visual showed PSNR value in 31.5460 dB, but wav file was only 3.8929 dB.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.