Step 0 - 輸入格式 (input format - single channel as example)
首先假設要處理的是 image 的某個 channel, multi-channel 的影像則僅是套用到多個 frame 或是以 channel 輸入, 同常有一張是 base image 這裡以 in 代表, 另一張為 reference image 以 ref 代表.
Step 1 - 邊界處理, 用來避免 search block 範圍超過邊界 (boundary handling, to avoid error of part of target block is out of frame)
Step 2 - 用以代表 block 內所有 input 與 reference 的 pixel 的座標 (setup the RDom that represent all pixels of corresponding input & refer block)
Func input = BoundaryConditions::repeat_edge(in);
Func refer = BoundaryConditions::repeat_edge(ref);
Step 2 - 用以代表 block 內所有 input 與 reference 的 pixel 的座標 (setup the RDom that represent all pixels of corresponding input & refer block)
// the position in motion vector output tableVar bid_x, bid_y;// variable for motion vectorVar mv_x, mv_y;RDom rblk(0, BLOCK_SIZE, 0, BLOCK_SIZE)
// position in input frame
Expr blk_x = (bid_x * BLOCK_SIZE) + rblk.x;Expr blk_y = (bid_y * BLOCK_SIZE) + rblk.y;
// position in reference frameExpr ref_x = blk_x + mv_x;
Expr ref_y = blk_y + mv_y;
Expr in_val = i16(input(blk_x, blk_y);Expr ref_val = i16(refer(ref_x, ref_y));Func sad;sad(mv_x, mv_y, bid_x, bid_y) = sum(abs(in_val - ref_val));
Step 4 - 搜尋與輸出, 這裡是透過 RDom + argmin 搭配來做比對搜尋並取得結果 (search and output, here we use RDom + argmin for matching and finding)
// setup search window
RDom search_win(-range/2, range, -range/2, range);// argmin will find the one in search_window which get minimal sad.Tuple me = argmin(sad(search_win.x, search_win.y, bid_x, bid_y));// output the motion estimation result to MV tableVar ch;out_mv(bid_x, bid_y, ch) = me[ch];
比較不直覺的地方在於兩個 RDom 套用的層次不同, 第一個 RDom 用來計算 matching criteria, 二個 RDom 是用來作為 motion estimation 尋找 mv, 總之這是以 search window 的方式作窮舉的 block matching 定義, 然而可以延伸與改進用在一開始所說的各種 matching 演算法上
