A Framework for Advanced Video Traces: Evaluating Visual Quality for Video Transmission Over Lossy Networks

Abstract

Conventional video traces (which characterize the video encoding frame sizes in bits and frame quality in PSNR) are limited to evaluating loss-free video transmission. To evaluate robust video transmission schemes for lossy network transport, generally experiments with actual video are required. To circumvent the need for experiments with actual videos, we propose in this paper an advanced video trace framework. The two main components of this framework are (i) advanced video traces which combine the conventional video traces with a parsimonious set of visual content descriptors, and (ii) quality prediction schemes that based on the visual content descriptors provide an accurate prediction of the quality of the reconstructed video after lossy network transport. We conduct extensive evaluations using a perceptual video quality metric as well as the PSNR in which we compare the visual quality predicted based on the advanced video traces with the visual quality determined from experiments with actual video. We find that the advanced video trace methodology accurately predicts the quality of the reconstructed video after frame losses.

No-Reference Quality Assessment for Networked Video via Primary Analysis of Bit Stream

“…To estimate the mean squared error (MSE) due to packet loss, three methods were proposed in [18] with different levels of access to bit stream. A more recent piece of work presented mathematical foundations and quality predictors for decoders to conceal losses by copying or freezing [19]. Other recent work aims at predicting the visibility of a packet loss to the HVS [20]- [22], where the important issue of accurately evaluating the impact of packet loss on the quality of each frame has not been fully addressed.…”

“…T n (19) where S d is the set of integer indexes to the successfully decoded and displayed frames of the transported digital video, and S d ⊆ S where S is the set of integer indexes to all frames of the original digital video. S d = S if and only if all frames of the original digital video have been successfully transported, decoded, and displayed in time.…”

Packet Loss in Real-Time Services: Markovian Models Generating QoE Impairments

“…by evaluating the blockiness of a frame, as a common artifact in block-based compression algorithms such as MPEG; Reduced-reference (RR) metrics evaluate the test video based on a subset of features previously extracted from the reference video. Lotfallah et al [18] propose a RR metric to evaluate the visual quality based on traces which are enriched with frame and motion related information. The proposed metric is based on frame dependencies and models the impact of a packet loss by considering the error propagation of the affected frame, e.g.…”

Perceptual-based Quality Metrics for Image and Video Services: A Survey

“…In [51] the concept of advanced video traces is introduced for MPEG-4 video streams. The key idea is to extend the set of available parameters in conventional video traces, which provide for instance information on frame size (in bits) and frame type (I/P/B), with a set of motion-related content descriptors.…”