10,368 research outputs found
JND-Based Perceptual Video Coding for 4:4:4 Screen Content Data in HEVC
The JCT-VC standardized Screen Content Coding (SCC) extension in the HEVC HM
RExt + SCM reference codec offers an impressive coding efficiency performance
when compared with HM RExt alone; however, it is not significantly perceptually
optimized. For instance, it does not include advanced HVS-based perceptual
coding methods, such as JND-based spatiotemporal masking schemes. In this
paper, we propose a novel JND-based perceptual video coding technique for HM
RExt + SCM. The proposed method is designed to further improve the compression
performance of HM RExt + SCM when applied to YCbCr 4:4:4 SC video data. In the
proposed technique, luminance masking and chrominance masking are exploited to
perceptually adjust the Quantization Step Size (QStep) at the Coding Block (CB)
level. Compared with HM RExt 16.10 + SCM 8.0, the proposed method considerably
reduces bitrates (Kbps), with a maximum reduction of 48.3%. In addition to
this, the subjective evaluations reveal that SC-PAQ achieves visually lossless
coding at very low bitrates.Comment: Preprint: 2018 IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP 2018
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
We propose a method for lossy image compression based on recurrent,
convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000,
and JPEG as measured by MS-SSIM. We introduce three improvements over previous
research that lead to this state-of-the-art result. First, we show that
training with a pixel-wise loss weighted by SSIM increases reconstruction
quality according to several metrics. Second, we modify the recurrent
architecture to improve spatial diffusion, which allows the network to more
effectively capture and propagate image information through the network's
hidden state. Finally, in addition to lossless entropy coding, we use a
spatially adaptive bit allocation algorithm to more efficiently use the limited
number of bits to encode visually complex image regions. We evaluate our method
on the Kodak and Tecnick image sets and compare against standard codecs as well
recently published methods based on deep neural networks
Generative Compression
Traditional image and video compression algorithms rely on hand-crafted
encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the
data being compressed. Here we describe the concept of generative compression,
the compression of data using generative models, and suggest that it is a
direction worth pursuing to produce more accurate and visually pleasing
reconstructions at much deeper compression levels for both image and video
data. We also demonstrate that generative compression is orders-of-magnitude
more resilient to bit error rates (e.g. from noisy wireless channels) than
traditional variable-length coding schemes
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