<title>Corruption model of loss propagation for relative prioritized packet video</title>

Abstract

Several analytical models have been recently introduced to estimate the impact of the error propagation effect on the source video caused by lossy transmission channels. However, previous work focused either on the statistical aspects for the whole sequence or had a high computational complexity. In this work, we concentrate on estimating the distortion caused by the loss of a packet with a moderate computational complexity. The proposed model considers both the spatial filtering effect and the temporal dependency that affect the error propagation behavior. To verify this model, a real loss propagation effect is measured and compared with that of the expected distortion level derived by the model. Also, its applicability to the quality of service (QoS) of transmitted video is demonstrated through the packet video evaluation over the simulated differentiated service (DiffServ) forwarding mechanism.

Error Resilient Video Compression Using Behavior Models

“…(14) Figure 6 shows the experimentally obtained RD curve together with a fitted curve representing our model (14). Since this RD curve should not only be valid for varying bit rate r i but also for varying propagated quantization distortion D q (r i−1 ), we also vary the bit rate of the previous frame r i−1 .…”

MPEG video markup language and its applications to robust video transmission

“…In general, the importance of an MB is proportional to the extent to which it is used in the motion compensated prediction. We adopt the notion of dependency weight, proposed by Kim et al in [28], to quantify the importance of each MB. Let M n,i denote the ith MB in frame n. Then, the dependency weight W n,i (j) is defined as the normalized number of pixels in M n,i that are used to predict pixels in M n + 1,j .…”

“…In this work, the distribution is modeled by the Gaussian distribution with mean l and variance r 2 . The importance indices are computed from MVs as described in [28], and their sample mean and variance are obtained and updated during the encoding. We select a threshold h (k) = l + kr 2 , which is parameterized by k. Then, the data for MBs are recorded in the partial MPML description, if their importance indices are higher than the threshold h (k).…”

Improved rate control for MPEG-4 video transport over wireless channel

“…In [11], an error propagation model is developed to track every MBs error propagation behavior. They use this model for intra refreshment and prioritizing video packets.…”