Although H.264 video coding standard provides several error resilience tools, the damage caused by error propagation may still be tremendous. This work is aimed at developing a robust and standard-compliant error resi...Although H.264 video coding standard provides several error resilience tools, the damage caused by error propagation may still be tremendous. This work is aimed at developing a robust and standard-compliant error resilient coding scheme for H.264and uses techniques of mode decision, data hiding, and error concealment to reduce the damage from error propagation. This paper proposes a system with two error resilience techniques that can improve the robustness of H.264 in noisy channels. The first technique is Nearest Neighbor motion compensated Error Concealment (NNEC) that chooses the nearest neighbors in the reference frames for error concealment. The second technique is Distortion Estimated Mode Decision (DEMD) that selects an optimal mode based on stochastically distorted frames. Observed simulation results showed that the rate-distortion performances of the proposed algorithms are better than those of the compared algorithms.展开更多
文摘Although H.264 video coding standard provides several error resilience tools, the damage caused by error propagation may still be tremendous. This work is aimed at developing a robust and standard-compliant error resilient coding scheme for H.264and uses techniques of mode decision, data hiding, and error concealment to reduce the damage from error propagation. This paper proposes a system with two error resilience techniques that can improve the robustness of H.264 in noisy channels. The first technique is Nearest Neighbor motion compensated Error Concealment (NNEC) that chooses the nearest neighbors in the reference frames for error concealment. The second technique is Distortion Estimated Mode Decision (DEMD) that selects an optimal mode based on stochastically distorted frames. Observed simulation results showed that the rate-distortion performances of the proposed algorithms are better than those of the compared algorithms.
