Yun Zhang, Na Li, Sam Kwong, Gangyi Jiang, and Huanqiang Zeng

ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM), Mar. 2019.

In this article, statistical Early Termination (ET) and Early Skip (ES) models are proposed for fast Coding Unit (CU) and prediction mode decision in HEVC INTRA coding, in which three categories of ET and ES sub-algorithms are included. First, the CU ranges of the current CU are recursively predicted based on the texture and CU depth of the spatial neighboring CUs. Second, the statistical model based ET and ES schemes are proposed and applied to optimize the CU and INTRA prediction mode decision, in which the coding complexities over different decision layers are jointly minimized subject...

Chunling Fan, Yun Zhang, Huan Zhang, Raouf Hamzaoui, and Qingshan Jiang

Journal of Visual Communication and Image Representation (JVCI), 2019.

The Picture-level Just Noticeable Difference (PJND) for a given image and compression scheme reflects the smallest distortion level that can be perceived by an observer with respect to a reference image. Previous work has focused on the PJND of images and videos. In this paper, we study the PJND of symmetrically and asymmetrically compressed stereoscopic images for JPEG2000 and H.265 intra coding. We conduct interactive subjective quality assessment tests to determine the PJND point using both a pristine image and a distorted image as a reference...

Na Li, Yun Zhang, Linwei Zhu, Wenhan Luo, Sam Kwong

Journal of Visual Communication and Image Representation (JVCI), 2019.

High Efficiency Video Coding (HEVC) is the ongoing video coding standard developed by the Joint Collaborative Team on Video Coding (JCT-VC), which makes a big step on compression efficiency to reduce half bit rate of H.264/AVC while maintaining the same video quality. The advantage of HEVC is the compression capability of supporting 4K Ultra High Definition (UHD) of 3840 × 2160 or 4096 × 2160 resolutions, and up to 8K UHD of 8192 × 4320 resolution. However, the deployment complexity of HEVC restricts its worldwide application in many emerging real-time applications, such as live video...

Huan Zhang, Yun Zhang*, Hanli Wang, Yo-Sung Ho and Shengzhong Feng

IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 561-576, Feb. 2019

In this paper, we propose a Weighted Local sparse representation based Depth Image Super-Resolution (WLDISR) schemes aiming at improving the Virtual View Image (VVI) quality of 3D video system. Different from color images, depth images are mainly used to provide geometrical information in synthesizing VVI. Due to the view synthesis characteristics difference between textural structures and smooth regions of depth images, we divide the depth images into edge and smooth patches and learn two local dictionaries, respectively. Meanwhile, the weight term is derived and incorporated explicitly in the cost function to denote different importance of edge structures and smooth regions to the VVI quality...

Chunling Fan, Yun Zhang, Raouf Hamzaoui, and Qingshan Jiang

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, May 2019.

The Just Noticeable Difference (JND) reveals the minimum distortion that the Human Visual System (HVS) can perceive. Traditional studies on JND mainly focus on background luminance adaptation and contrast masking. However, the HVS does not perceive visual content based on individual pixels or blocks, but on the entire image. In this work, we conduct an interactive subjective visual quality study on the Picture-level JND (PJND) of compressed stereo images...

Chunling Fan, Hanhe Lin, Vlad Hosu, Yun Zhang, Qingshan Jiang, Raouf Hamzaoui, and Dietmar Saupe

Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Berlin, June 2019.

The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves...