In this paper, we present an encoder-aware motion compensated temporal pre-processing filter (EA-MCTF) that adapts the filter on a block-basis based upon the spatio-temporal content properties and block-level encoding parameters. Some sample parameters include block-level QP, variance and mean-squared error of motion compensated block difference, slice types of adjoining frames, and frequency of a block being used as a reference. Applying the EA-MCTF to a HEVC encoder yields -12.4% average VMAF BD-rate savings over unfiltered encodings, and furthermore, the EA-MCTF yields a superior BD-rate and computational complexity performance over the MCTF available in the HEVC reference software.
In web streaming, the size of video rendered on screen may be influenced by a number of factors, such as the layout of a web page embedding the video, the position and size of the web browser window, and the resolution of the screen. During the playback, the adaptive streaming players, usually select one of the available encoded streams (renditions) to pull and render on the screen. Such selection is typically done based on the available network bandwidth, and also based on the size of the player window. Typically, the logic of matching video stream to be played to the size of the window is very simplistic, considering only pixel dimensions of the video. However, with vastly different video playback devices, their pixel densities and other parameters influencing the Quality of Experience (QoE), the reliance of pixel matching is bound to be suboptimal. A better approach must use a proper QoE model, considering parameters of viewing setup on each device, and then predicting which encoded resolution, given player window and other constraints would achieve best quality. In this paper, we adopt such a model and develop an optimal rendition selection algorithm based on it. We report results by considering several different categories of receiving devices (HDTV, PCs, tablets and mobile) and show that optimal selections in all those cases will be considerably different.
In a multi-generation transcoding system, the source may be an encoded mezzanine video whose objective video quality metrics (e.g., PSNR, SSIM) are unknown. Transcoding process yields objective quality metrics that are relative to the encoded source video, which does not indicate the actual quality of the transcoded video relative to the original uncompressed reference video. In this paper, we present an approach for estimating the objective quality metrics of the encoded mezzanine and demonstrate that it has higher accuracy compared to a well-known scheme. Finally, we derive bounds for the end-to-end objective quality metrics of the transcoded video, and use it for controlling the transcoding process to ensure that the final transcoded video satisfies a quality criterion.
Recently, there have been efforts by the ITU-T VCEG and ISO/IEC MPEG to further improve the compression
performance of the High Efficiency Video Coding (HEVC) standard for developing a potential next generation video
coding standard. The exploratory codec software of this potential standard includes new coding tools for inter and intra
coding. In this paper, we present a new intra prediction mode for lossless intra coding. Our new intra mode derives a
prediction filter for each input pixel using its neighboring reconstructed pixels, and applies this filter to the nearest
neighboring reconstructed pixels to generate a prediction pixel. The proposed intra mode is demonstrated to improve the
performance of the exploratory software for lossless intra coding, yielding a maximum and average bitrate savings of 4.4%
and 2.11%, respectively.
The ultimate goal of network resource allocation for video teleconferencing is to optimize the Quality of Experience
(QoE) of the video. The IPPP video coding structure with macroblock intra refresh is widely used for video
teleconferencing. With such video coding structure, the loss of a frame generally causes error propagation to
subsequent frames. A resource allocation decision of a communication network determines the QoE given that
other conditions such as viewing conditions are fixed. Therefore, to optimize the QoE, a communication network
needs to be able to accurately predict the QoE for each of its resource allocation decisions and then selects the
decision corresponding to the best QoE. In our previous work, we reduced the QoE prediction problem to one of
predicting the per-frame PSNR time series. The accuracy of the proposed per-frame PSNR prediction method
was demonstrated, however, only for low resolution video sequences. In this paper, we show via simulations
that the per-frame PSNR prediction method achieves good performance for higher resolution video sequences as
well.
Upsampling is a post-processing method for increasing the spatial resolution of an image or video. Most video
players and image viewers support upsampling functionality. Sometimes upsampling can introduce blurring,
ringing, and jaggedness artifacts in the upsampled video or image thereby lowering its visual quality. In this paper,
we present an adaptive bilateral interpolation filter for upsampling a video or image by an arbitrary upsampling
factor, and show that it mitigates most of the artifacts produced by conventional upsampling methods.
In this paper, we present an error resilient video coding scheme for wireless video telephony applications that uses
feedback to limit error propagation. In conventional feedback-based error resilient schemes, error propagation
can significantly degrade visual quality when feedback delay is in the order of a few seconds. We propose a coding
structure based on multiple description coding that mitigates error propagation during feedback delay, and uses
feedback to adapt its coding structure to effectively limit error propagation. We demonstrate the effectiveness
of our approach at different error rates when compared to conventional coding schemes that use feedback.
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