Using Auto-Regressive Model with Multiple Training Window Sizes in Multiple Description Coding

• Main Authors: Wang, Ching-Yen, 王敬嚴
• Other Authors: Tsai, Wen-Jiin
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/48546022850410142896

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 101 === Since network video streaming have become popular in recent years, error resilient technique is more important. Multiple description video coding is one of well-known error resilient methods to cope with the network erroneous transmission in various networks environments. In conventional auto-regressive model, fixed training window size is adopted. In this thesis, we design a multiple description coding which adopts an auto-regressive model with multiple training window sizes to enhance the error resilience. In our MDC structure, we encode a video stream into two descriptions; one description contains all odd frames and the other contains all even frames. Both are encoded according to H.264/AVC standard. In the decoder side, we recover missing frames by using auto-regressive model with selected training window sizes. According to the experimental results, the proposed method outperforms other methods in both objective and subjective quality.