1,625 research outputs found
Perceptually-Driven Video Coding with the Daala Video Codec
The Daala project is a royalty-free video codec that attempts to compete with
the best patent-encumbered codecs. Part of our strategy is to replace core
tools of traditional video codecs with alternative approaches, many of them
designed to take perceptual aspects into account, rather than optimizing for
simple metrics like PSNR. This paper documents some of our experiences with
these tools, which ones worked and which did not. We evaluate which tools are
easy to integrate into a more traditional codec design, and show results in the
context of the codec being developed by the Alliance for Open Media.Comment: 19 pages, Proceedings of SPIE Workshop on Applications of Digital
Image Processing (ADIP), 201
Visually Lossless Perceptual Image Coding Based on Natural- Scene Masking Models
Perceptual coding is a subdiscipline of image and video coding that uses models of human visual perception to achieve improved compression efficiency. Nearly, all image and video coders have included some perceptual coding strategies, most notably visual masking. Today, modern coders capitalize on various basic forms of masking such as the fact that distortion is harder to see in very dark and very bright regions, in regions with higher frequency content, and in temporal regions with abrupt changes. However, beyond these obvious forms of masking, there are many other masking phenomena that occur (and co-occur) when viewing natural imagery. In this chapter, we present our latest research in perceptual image coding using natural-scene masking models. We specifically discuss: (1) how to predict local distortion visibility using improved natural-scene masking models and (2) how to apply the models to high efficiency video coding (HEVC). As we will demonstrate, these techniques can offer 10â20% fewer bits than baseline HEVC in the ultra-high-quality regime
Watermarking for multimedia security using complex wavelets
This paper investigates the application of complex wavelet transforms to the field of digital data hiding. Complex wavelets offer improved directional selectivity and shift invariance over their discretely sampled counterparts allowing for better adaptation of watermark distortions to the host media. Two methods of deriving visual models for the watermarking system are adapted to the complex wavelet transforms and their performances are compared. To produce improved capacity a spread transform embedding algorithm is devised, this combines the robustness of spread spectrum methods with the high capacity of quantization based methods. Using established information theoretic methods, limits of watermark capacity are derived that demonstrate the superiority of complex wavelets over discretely sampled wavelets. Finally results for the algorithm against commonly used attacks demonstrate its robustness and the improved performance offered by complex wavelet transforms
Geometry-based spherical JND modeling for 360 display
360 videos have received widespread attention due to its realistic
and immersive experiences for users. To date, how to accurately model the user
perceptions on 360 display is still a challenging issue. In this paper,
we exploit the visual characteristics of 360 projection and display and
extend the popular just noticeable difference (JND) model to spherical JND
(SJND). First, we propose a quantitative 2D-JND model by jointly considering
spatial contrast sensitivity, luminance adaptation and texture masking effect.
In particular, our model introduces an entropy-based region classification and
utilizes different parameters for different types of regions for better
modeling performance. Second, we extend our 2D-JND model to SJND by jointly
exploiting latitude projection and field of view during 360 display.
With this operation, SJND reflects both the characteristics of human vision
system and the 360 display. Third, our SJND model is more consistent
with user perceptions during subjective test and also shows more tolerance in
distortions with fewer bit rates during 360 video compression. To
further examine the effectiveness of our SJND model, we embed it in Versatile
Video Coding (VVC) compression. Compared with the state-of-the-arts, our
SJND-VVC framework significantly reduced the bit rate with negligible loss in
visual quality
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