Improved Estimation of Transmission Distortion for Error-Resilient Video Coding

Abstract

Abstract-This paper presents an improved technique for estimating the end-to-end distortion, which includes both quantization error after encoding and random transmission error, after transmission in video communication systems. The proposed technique mainly differs from most existing techniques in that it takes into account filtering operations, e.g. interpolation in subpixel motion compensation, as introduced in advanced video codecs. The distortion estimation for pixels or subpixels under filtering operations requires the computation of the second moment of a weighted sum of random variables. In this paper, we prove a proposition for calculating the second moment of a weighted sum of correlated random variables without requiring knowledge of their probability distribution. Then, we apply the proposition to extend our previous error-resilient algorithm for prediction mode decision without significantly increasing complexity. Experimental results using an H.264/AVC codec show that our new algorithm provides an improvement in both rate-distortion performance and subjective quality over existing algorithms. Our algorithm can also be applied in the upcoming high efficiency video coding (HEVC) standard, where additional filtering techniques are under consideration.

End User Video Quality Prediction and Coding Parameters Selection at the Encoder for Robust HEVC Video Transmission

“…Furthermore, as a consequence of propagated channel error, certain pixel values at the decoder could exceed their nominal range (i.e., for 8 bit precise videos, nominal range of pixels values are between 0 and 255) in which case the decoder performs a clipping operation to retain them within the nominal range. This behavior had not been foreseen by the previous quality prediction methods and was incorporated in the algorithm proposed in [5]. However, the applicability of these algorithms in the context of HEVC is limited due to the fact that these methods do not possess the intelligence to capture the error propagation behavior introduced by the novel components of HEVC such as AMVP.…”

SSIM-based cross-layer optimized video streaming over LTE downlink

“…))] 2 , (8) where N sb is the number of the subcarrier, γ j is the SINR at the j th subcarrier, and κ(m) is the calibration factor for the MCS mode m. The functions J(x) and J −1 (y) are defined as (9) and (10). Based on the MIESM γ mief f (m), the BLER BLER(r mief f (m)) for the RB with the MCS mode m can be accurately predicted as [15] BLER(γ mief f (m)) =…”

“…In [7], the Recursive Optimal Per-pixel Estimation (ROPE) was utilized to estimate the end-to-end distortion, which assisted RDO to determine the optimal encoding modes in the error-prone transmission environment. To improve the decoding distortion estimation accuracy, the effect of filtering operation on the accuracy of the distortion estimation was well considered by Chen et al [9]. In our previous work [10], the structural similarity was introduced into the error-resilient RDO process as the distortion metric, and the Lagrange multiplier was correspondingly adjusted.…”

End to end video distortion estimation with advanced error concealment considerations

“…In our model the value of ↵ i for inter coded pixels is estimated using the Laplacian function method proposed in [12] wherein the decaying effect of error propagation is attributed to clipping noise. The value that ↵ i can get using this method ranges from 0.5 to 1.…”