Motion Compensated Temporal Filtering (MCTF) is a pre-processing approach employed prior to video encoding, for improving the compression efficiency. Prior MCTF designs (e.g. [1]) use pre-defined frame-level quantization parameters (QPs) for different slice types and temporal layers, and operate with a fixed Group of Pictures (GOP) structure. However, commercial encoders can adapt GOP structure based upon content characteristics, and can also adapt QPs on a block-basis based upon the frequency of the block being referenced and the spatial complexity of the block, causing prior MCTF to perform sub-optimally with commercial encoders.

This paper describes a video coding scheme submitted in response to the joint call for proposals (CfP) on video compression for capability beyond HEVC issued by ITU-T SG16 Q.6 (VCEG) and ISO/IEC JTC1/SC29/WG11(MPEG) in October 2017. It includes video coding techniques for the standard dynamic range (SDR) and high dynamic range (HDR) categories. Design of the core SDR codec in the response is based on the joint exploration model (JEM) reference software developed by the joint video exploration team (JVET). Some of key coding tools in the JEM are significantly simplified to reduce both average and worst-case complexity for hardware design with negligible coding performance loss. Furthermore, two additional coding technologies, namely multi-type tree (MTT) and decoder-side intra mode derivation (DIMD), are used to further improve coding efficiency. For the HDR category, besides the tools used in SDR category, two additional coding tools: an in-loop reshaper and a luma-based QP prediction method are used to further improve HDR coding efficiency. Simulation results demonstrate the high coding efficiency achieved by the proposed video codec at the expense of moderate coding complexity over HEVC. For random access configuration, it achieves average bit rate savings of 35.7% and 4.00% over the HM and JEM anchors with decoding time of 263% and 33%, respectively, for the SDR sequences. For the HDR sequences, the proposed in-loop reshaper is configured to maximize HDR objective metrics, it achieves average bit rate savings of 31.3% and 4.6% over the HM and JEM for wPSNRY metrics for the HDR PQ content.

We describe the design of a perceptual preprocessing filter for improving the effectiveness of video coding. This filter uses known parameters of the reproduction setup, such as viewing distance, pixel density, and contrast ratio of the screen, as well as a contrast sensitivity model of human vision to identify spatial oscillations that are invisible. By removing such oscillations the filter simplifies the video content, therefore leading to more efficient encoding without causing any visible alterations of the content. Through experiments, we demonstrate the use of our filter can yield significant bit rate savings compared to conventional encoding methods that are not tailored to specific viewing conditions.

The H.264 encoder has input parameters that determine the bit rate and distortion of the compressed video and the encoding complexity. A set of encoder parameters is referred to as a parameter setting. We previously proposed two offline algorithms for choosing H.264 encoder parameter settings that have distortion-complexity performance close to the parameter settings obtained from an exhaustive search, but take significantly fewer encodings. However they generate only a few parameter settings. If there is no available parameter settings for a given encode time, the encoder will need to use a lower complexity parameter setting resulting in a decrease in peak-signal-to-noise-ratio (PSNR). In this paper, we propose two algorithms for finding additional parameter settings over our previous algorithm and show that they improve the PSNR by up to 0.71 dB and 0.43 dB, respectively. We test both our algorithms on Linux and PocketPC platforms.

The H.264/ACV standard provides significant improvements in performance over earlier video coding standards at the cost of increased complexity. Our challenge is to determine H.264 parameter settings that have low complexity but still offer high video quality. In this paper, we propose two fast algorithms for finding the H.264 parameter settings that take about 1% and 8%, respectively, of the number of tests required by an exhaustive search. Both the fast algorithms result in a maximum decrease in peak-signal-to-noise ratio of less than 0.71 dB for different data sets and bitrates