低復(fù)雜度的圖像信源-信道聯(lián)合譯碼算法
Low-Complexity Joint Source Channel Decoding of Image
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摘要: 該文提出將圖像編碼后殘留冗余的馬爾可夫場(chǎng)模型分解為4個(gè)方向的馬爾可夫鏈，并結(jié)合簡(jiǎn)化的模型及低密度奇偶校驗(yàn)碼(LDPC)譯碼的軟輸出進(jìn)行信源-信道聯(lián)合譯碼。將分解后信源中多個(gè)方向上同時(shí)存在的相關(guān)性看作一種特殊的天然信道編碼方式，利用前向-后向算法、和積算法以及信道譯碼軟輸出分別對(duì)信源符號(hào)進(jìn)行串行和并行的譯碼。仿真實(shí)驗(yàn)表明，與傳統(tǒng)利用馬爾可夫場(chǎng)模型的聯(lián)合譯碼算法相比，該聯(lián)合譯碼算法降低了復(fù)雜度，同時(shí)提高了重建圖像的峰值信噪比。Abstract: Markov Random Field Model (MRFM) is separated into four Markov chains which are used to represent the residuals of encoded image source. Combined with the soft output of Low Density Parity Check (LDPC) code, this simplified model is used in joint source channel decoding. Different correlation in different direction in source is regarded as a kind of natural channel code. In order to utilize the correlation, a serial decoding using forward-backward algorithm and a parallel decoding using sum-product algorithm are proposed respectively. Simulations show that compared with the traditional joint source channel decoding algorithm based on the MRFM, the proposed algorithm has lower complexity and better PSNR of the rebuilt images.
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