VP9 Encoder Research Articles

Speeding up VP9 Intra Encoder with Hierarchical Deep Learning Based Partition Prediction.

In VP9 video codec, the sizes of blocks are decided during encoding by recursively partitioning 64×64 superblocks using rate-distortion optimization (RDO). This process is computationally intensive because of the combinatorial search space of possible partitions of a superblock. Here, we propose a deep learning based alternative framework to predict the intra-mode superblock partitions in the form of a four-level partition tree, using a hierarchical fully convolutional network (H-FCN). We created a large database of VP9 superblocks and the corresponding partitions to train an H-FCN model, which was subsequently integrated with the VP9 encoder to reduce the intra-mode encoding time. The experimental results establish that our approach speeds up intra-mode encoding by 69.7% on average, at the expense of a 1.71% increase in the Bjøntegaard-Delta bitrate (BD-rate). While VP9 provides several built-in speed levels which are designed to provide faster encoding at the expense of decreased rate-distortion performance, we find that our model is able to outperform the fastest recommended speed level of the reference VP9 encoder for the good quality intra encoding configuration, in terms of both speedup and BD-rate.

Deep Video Precoding

Several groups worldwide are currently investigating how deep learning may advance the state-of-the-art in image and video coding. An open question is how to make deep neural networks work in conjunction with existing (and upcoming) video codecs, such as MPEG H.264/AVC, H.265/HEVC, VVC, Google VP9 and AOMedia AV1, AV2, as well as existing container and transport formats, without imposing any changes at the client side. Such compatibility is a crucial aspect when it comes to practical deployment, especially when considering the fact that the video content industry and hardware manufacturers are expected to remain committed to supporting these standards for the foreseeable future. We propose to use deep neural networks as precoders for current and future video codecs and adaptive video streaming systems. In our current design, the core precoding component comprises a cascaded structure of downscaling neural networks that operates during video encoding, prior to transmission. This is coupled with a precoding mode selection algorithm for each independently-decodable stream segment, which adjusts the downscaling factor according to scene characteristics, the utilized encoder, and the desired bitrate and encoding configuration. Our framework is compatible with all current and future codec and transport standards, as our deep precoding network structure is trained in conjunction with linear upscaling filters (e.g., the bilinear filter), which are supported by all web video players. Extensive evaluation on FHD (1080p) and UHD (2160p) content and with widely-used H.264/AVC, H.265/HEVC and VP9 encoders, as well as a preliminary evaluation with the current test model of VVC (v.6.2rc1), shows that coupling such standards with the proposed deep video precoding allows for 8% to 52% rate reduction under encoding configurations and bitrates suitable for video-on-demand adaptive streaming systems. The use of precoding can also lead to encoding complexity reduction, which is essential for cost-effective cloud deployment of complex encoders like H.265/HEVC, VP9 and VVC, especially when considering the prominence of high-resolution adaptive video streaming.

VP9 Encoder and Decoders for Next-Generation Online Video Platforms and Services

VP9 Encoder and Decoders for Next-Generation Online Video Platforms and Services

Quality comparison of the HEVC and VP9 encoders performance

This paper reports a comparison between two recent video codecs, namely the HEVC and the VP9, using High Definition Video Sequences encoded with different bit rates. A subjective test for the evaluation of the provided Quality of Experience is reported. The video sequences were shown to a panel of subjects on a High Definition LED display and the subjective tests were performed using a Single Stimulus Methodology. The results shown that the HEVC encoder provides a better visual quality on low bit rates than the VP9. Similar performance was obtained for visually lossless conditions, although the HEVC requires lower bit rates to reach that level. Moreover, the correlation of the subjective evaluation and three tested objective metrics (PSNR, SSIM, and FSIM) revealed a good representation of the subjective results, particularly the SSIM and the FSIM metrics.

Fast video transcoding from HEVC to VP9

HEVC and VP9 are the current state-of-the-art in video compression, since their bit-streams were recently finalized in January and May 2013, respectively. These codecs are the generational successors of the currently most widelyused video codecs, H.264/AVC and VP8, respectively. Consequently, it is expected that in the near future these new codecs will replace their predecessors. However, the process of converting video contents compressed with one standard to those using another standard is highly computationally expensive, since a priori the video contents must be decompressed and compressed with the target video encoder. Nevertheless, it is known that some information can be extracted from the decoding process in order to accelerate the encoding process. In this paper, some techniques for transcoding from HEVC to VP9 are presented. By using some information extracted from the HEVC decoding process some coding modes will be discarded from being checked in the VP9 encoder. By applying the proposed approaches, a reduction of about 60% of the coding complexity is achieved with acceptable Rate Distortion penalties.

Intra compression efficiency in VP9 and HEVC

The amount of video data stored on local devices or transmitted over the networks is permanently increased. The emerging of a more efficient next generation video coding standard is of a high demand at the moment. There seem to be two main contenders for the position of the next state-of-the-art video compression standard: JCT-VC H.265/HEVC and Google VP9. The announced aim of HEVC is to achieve twice more efficient compression compared to H.264/AVC, and VP9 was developed to get half the bit-rate of VP8 with royalty-free video 6804 M.P. Sharabayko, O.G. Ponomarev and R.I. Chernyak codec. Intra compression is one of the main features that determines the compression efficiency of the whole codec. In this paper we get into detailed overview of intra compression data-flow in HEVC and VP9. We describe common and unique stages of both standards. Then we carry out experiments with JCT-VC HM and WebM VP9 encoders on intra compression efficiency. We also turn some of the HEVC features off to get its dataflow as close to VP9 as possible. Finally we get into discussion of the efficiency of both codecs, the corresponding standards and their intra compression algorithms. Mathematics Subject Classification: 94A08