A two-stage video coding framework with both self-adaptive redundant dictionary and adaptively orthonormalized DCT basis

In this work, we propose a two-stage video coding framework, as an extension of our previous one-stage framework in [1]. The two-stage frameworks consists two different dictionaries. Specifically, the first stage directly finds the sparse representation of a block with a self-adaptive dictionary consisting of all possible inter-prediction candidates by solving an L0-norm minimization problem using an improved orthogonal matching pursuit with embedded orthonormalization (eOMP) algorithm, and the second stage codes the residual using DCT dictionary adaptively orthonormalized to the subspace spanned by the first stage atoms. The transition of the first stage and the second stage is determined based on both stages' quantization stepsizes and a threshold. We further propose a complete context adaptive entropy coder to efficiently code the locations and the coefficients of chosen first stage atoms. Simulation results show that the proposed coder significantly improves the RD performance over our previous one-stage coder. More importantly, the two-stage coder, using a fixed block size and inter-prediction only, outperforms the H.264 coder (x264) and is competitive with the HEVC reference coder (HM) over a large rate range

A novel method for subjective picture quality assessment and further studies of HDTV formats

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ IEEE 2008.This paper proposes a novel method for the assessment of picture quality, called triple stimulus continuous evaluation scale (TSCES), to allow the direct comparison of different HDTV formats. The method uses an upper picture quality anchor and a lower picture quality anchor with defined impairments. The HDTV format under test is evaluated in a subjective comparison with the upper and lower anchors. The method utilizes three displays in a particular vertical arrangement. In an initial series of tests with the novel method, the HDTV formats 1080p/50,1080i/25, and 720p/50 were compared at various bit-rates and with seven different content types on three identical 1920 times 1080 pixel displays. It was found that the new method provided stable and consistent results. The method was tested with 1080p/50,1080i/25, and 720p/50 HDTV images that had been coded with H.264/AVC High profile. The result of the assessment was that the progressive HDTV formats found higher appreciation by the assessors than the interlaced HDTV format. A system chain proposal is given for future media production and delivery to take advantage of this outcome. Recommendations for future research conclude the paper

Weighted Combination of Sample Based and Block Based Intra Prediction in Video Coding

The latest standard within video compression, HEVC/H.265, was released during 2013 and provides a significant improvement from its predecessor AVC/H.264. However, with a constantly increasing demand for high denition video and streaming of large video files, there are still improvements that can be done. Difficult content in video sequences, for example smoke, leaves and water that moves irregularly, is being hard to predict and can be troublesome at the prediction stage in the video compression. In this thesis, carried out at Ericsson in Stockholm, the combination of sample based intra prediction (SBIP) and block based intra prediction (BBIP) is tested to see if it could improve the prediction of video sequences containing difficult content, here focusing on water. The combined methods are compared to HEVC intra prediction. All implementations have been done in Matlab. The results show that a combination reduces the Mean Squared Error (MSE) as well as could improve the Visual Information Fidelity (VIF) and the mean Structural Similarity (MSSIM). Moreover the visual quality was improved by more details and less blocking artefacts

Scalable video transcoding for mobile communications

Mobile multimedia contents have been introduced in the market and their demand is growing every day due to the increasing number of mobile devices and the possibility to watch them at any moment in any place. These multimedia contents are delivered over different networks that are visualized in mobile terminals with heterogeneous characteristics. To ensure a continuous high quality it is desirable that this multimedia content can be adapted on-the-fly to the transmission constraints and the characteristics of the mobile devices. In general, video contents are compressed to save storage capacity and to reduce the bandwidth required for its transmission. Therefore, if these compressed video streams were compressed using scalable video coding schemes, they would be able to adapt to those heterogeneous networks and a wide range of terminals. Since the majority of the multimedia contents are compressed using H.264/AVC, they cannot benefit from that scalability. This paper proposes a technique to convert an H.264/AVC bitstream without scalability to a scalable bitstream with temporal scalability as part of a scalable video transcoder for mobile communications. The results show that when our technique is applied, the complexity is reduced by 98 % while maintaining coding efficiency

PEA265: Perceptual Assessment of Video Compression Artifacts

The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Qualityof- Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subjectlabelled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265 database, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. To objectively identify these PEAs, we train Convolutional Neural Networks (CNNs) using the PEA265 database. It appears that state-of-theart ResNeXt is capable of identifying each type of PEAs with high accuracy. Furthermore, we define PEA pattern and PEA intensity measures to quantify PEA levels of compressed video sequence. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.Comment: 10 pages,15 figures,4 table