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Rate-Distortion Optimized Motion-Compensated Prediction for Packet Loss Resilient Video Coding | IEEE Journals & Magazine | IEEE Xplore

Rate-Distortion Optimized Motion-Compensated Prediction for Packet Loss Resilient Video Coding


Abstract:

A rate-distortion optimized motion-compensated prediction method for robust video coding is proposed. Contrasting methods from the conventional literature, the proposed a...Show More

Abstract:

A rate-distortion optimized motion-compensated prediction method for robust video coding is proposed. Contrasting methods from the conventional literature, the proposed approach uses the expected reconstructed distortion after transmission, instead of the displaced frame difference in motion estimation. Initially, the end-to-end reconstructed distortion is estimated through a recursive per-pixel estimation algorithm. Then the total bit rate for motion-compensated encoding is predicted using a suitable rate distortion model. The results are fed into the Lagrangian optimization at the encoder to perform motion estimation. Here, the encoder automatically finds an optimized motion compensated prediction by estimating the best tradeoff between coding efficiency and end-to-end distortion. Finally, rate-distortion optimization is applied again to estimate the macroblock mode. This process uses previously selected optimized motion vectors and their corresponding reference frames. It also considers intraprediction. Extensive computer simulations in lossy channel environments were conducted to assess the performance of the proposed method. Selected results for both single and multiple reference frames settings are described. A comparative evaluation using other conventional techniques from the literature was also conducted. Furthermore, the effects of mismatches between the actual channel packet loss rate and the one assumed at the encoder side have been evaluated and reported in this paper
Published in: IEEE Transactions on Image Processing ( Volume: 16, Issue: 5, May 2007)
Page(s): 1327 - 1338
Date of Publication: 16 April 2007

ISSN Information:

PubMed ID: 17491463

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