Ching-Yeh Chen

Sample Adaptive Offset in the HEVC Standard

This paper provides a technical overview of a newly added in-loop filtering technique, sample adaptive offset (SAO), in High Efficiency Video Coding (HEVC). The key idea of SAO is to reduce sample distortion by first classifying reconstructed samples into different categories, obtaining an offset for each category, and then adding the offset to each sample of the category. The offset of each category is properly calculated at the encoder and explicitly signaled to the decoder for reducing sample distortion effectively, while the classification of each sample is performed at both the encoder and the decoder for saving side information significantly. To achieve low latency of only one coding tree unit (CTU), a CTU-based syntax design is specified to adapt SAO parameters for each CTU. A CTU-based optimization algorithm can be used to derive SAO parameters of each CTU, and the SAO parameters of the CTU are inter leaved into the slice data. It is reported that SAO achieves on average 3.5% BD-rate reduction and up to 23.5% BD-rate reduction with less than 1% encoding time increase and about 2.5% decoding time increase under common test conditions of HEVC reference software version 8.0.

Sample adaptive offset for HEVC

A new video coding tool, sample adaptive offset (SAO), is introduced in this paper. SAO has been adopted into the Working Draft of the new video coding standard, High-Efficiency Video Coding (HEVC). The SAO is located after deblocking in the video coding loop. The concept of SAO is to classify reconstructed pixels into different categories and then reduce the distortion by simply adding an offset for each category of pixels. The pixel intensity and edge properties are used for pixel classification. To further improve the coding efficiency, a picture can be divided into regions for localization of offset parameters. Simulation results show that SAO can achieve on average 2% bit rate reduction and up to 6% bit rate reduction. The run time increases for encoders and decoders are only 2%.

Adaptive Loop Filtering for Video Coding

Adaptive loop filtering for video coding is to minimize the mean square error between original samples and decoded samples by using Wiener-based adaptive filter. The proposed ALF is located at the last processing stage for each picture and can be regarded as a tool to catch and fix artifacts from previous stages. The suitable filter coefficients are determined by the encoder and explicitly signaled to the decoder. In order to achieve better coding efficiency, especially for high resolution videos, local adaptation is used for luma signals by applying different filters to different regions or blocks in a picture. In addition to filter adaptation, filter on/off control at coding tree unit (CTU) level is also helpful for improving coding efficiency. Syntax-wise, filter coefficients are sent in a picture level header called adaptation parameter set, and filter on/off flags of CTUs are interleaved at CTU level in the slice data. This syntax design not only supports picture level optimization but also achieves a low encoding latency. Simulation results show that the ALF can achieve on average 7% bit rate reduction for 25 HD sequences. The run time increases are 1% and 10% for encoders and decoders, respectively, without special attention to optimization in C++ code.

One-pass encoding algorithm for adaptive loop filter in high-efficiency video coding

In this paper, a one-pass encoding algorithm is proposed for adaptive loop filter (ALF) in high-efficiency video coding (HEVC). ALF can improve both subjective and objective video quality, but it also requires a lot of encoding passes (i.e. picture buffer accesses) that will significantly increase external memory access, encoding latency, and power consumption. Therefore, we propose a method to estimate filtering distortion without performing real filter operation. The number of encoding passes can be effectively reduced from 16 to 1. Combined with an initial guess of filter-on/off blocks by using time-delayed filters, the proposed one-pass algorithm only induces average 0.17% BD-rate increase.

The adaptive loop filtering techniques in the HEVC standard

This article introduces adaptive loop filtering (ALF) techniques being considered for the HEVC standard. The key idea of ALF is to minimize the mean square error between original pixels and decoded pixels using Wiener-based adaptive filter coefficients. ALF is located at the last processing stage of each picture and can be regarded as a tool trying to catch and fix artifacts from previous stages. The suitable filter coefficients are determined by the encoder and explicitly signaled to the decoder. In order to achieve better coding efficiency, especially for high resolution videos, local adaptation is used for luma signals by applying different filter to different region in a picture. In addition to filter adaptation, filter on/off control at largest coding unit (LCU) level is also helpful for improving coding efficiency. Syntax-wise, filter coefficients are sent in a picture level header called adaptation parameter set (APS), and filter on/off flags of LCUs are interleaved at LCU level in the slice data. Besides supporting picture-based optimization of ALF, the syntax design can support low delay applications as well. When the filter coefficients in APS are trained by using a previous picture, filter on/off decisions can be made on the fly during encoding of LCUs, so the encoding latency is only one LCU. Simulation results show that the ALF can achieve on average 5% bit rate reduction and up to 27% bit rate reduction for 25 HD sequences. The run time increases are 1% and 10% for encoders and decoders, respectively, with un-optimized C++ codes in software.