近幾年來QR碼在生活中已隨處可見，使用者只需使用手機的相關應用程式即可馬上知道QR碼內所藏的資訊，因其便利性有些商家將QR碼直接作為廣告文宣使用，甚至有些商家直接將自己的標誌覆蓋至QR碼上，使消費者可以知道QR碼內容與商家相關，但最大覆蓋的標誌大小將會依不同錯誤更正能力而定，若覆蓋標誌面積大於其錯誤更正能力，就會造成QR碼無法順利解碼。
本論文我們提出新的QR碼編解碼方式，將我們所需要的資訊透過標準QR碼編碼器進行編碼，決定好標誌大小、擺放位置和標誌參數後，將QR碼中被標誌覆蓋的位置之資料(under-logo data)搬移至不會被標誌覆蓋的位置(QR data)的LSB層(least significant bit)，將傳統二階的QR碼轉為四階灰度值的QR碼，本論文將這種QR碼簡稱為BLEQR(big-logo-embedded QR code QR code )。
在解碼部分我們將會模擬兩種場景進行解碼，分別為BLEQR碼在平面上變形的場景與柱面上變形的場景，在平面上變形的場景，我們利用交比[4](cross ratio)找出模組數，接著使用投影轉換(projective transform)將BLEQR影像回正，切割模組再將雜訊去除後回復為標準QR碼即可進行解碼，柱面上變形的場景則是先使用預測邊緣點和交比讀取出標誌參數，知道標誌參數後即可產生標誌地圖，接著排除標誌區域並對BLEQR影像做切割，最後將所有模組區分好後去除雜訊，即可使用標準QR碼解碼。依據上述的方法，我們可以增加傳統QR碼放置標誌的大小和成功解碼出平面與柱面上變形的BLEQR影像。

QR code has become very popular in recent years. By using related applications, users can get the data that hidden in it easily. Stores use OR code as a kind of advertisement due to its convenience. Furthermore, they could put their logos on QR code so that customers know the content relevant to them. However, the biggest size of the logo depends on the difference of error correction ability. If the logo size is far beyond the error correction ability, the QR code would not be decoded successfully.
The thesis proposes new approaches to encode and decode the big-logo-embedded QR codes. First, we encode the data through standard QR code encoder. After deciding the size, position and shape parameters of the logo, we move the data that are covered by logo (referred to as under-logo data) to LSB (least-significant-bit) bit plane of the data which is not covered by logo (QR data). Transforming traditional binary QR code to four level gray modules QR code is abbreviated to BLEQR code (referred to as big-logo-embedded QR code) in this thesis.
We simulate two scenarios in decoding. Both of them are the deformations of BLEQR code but one is for the planes and the other is for the cylinders. We find the module number by cross ratio in plane deformation. Then, use the projective transform to rectify BLEQR image. After segmenting the modules and eliminating the noise, it returns to the standard QR code. In cylinder deformation, we use edge point prediction and cross ratio to get the shape parameters. Hence, we can get the logo map. After eliminating the edge of logo areas, we segment the remaining modules and remove the noise. Finally, we can decode it by the standard QR code decoder. According to the approaches mentioned above, we not only increase the logo size on the QR code but also successfully decode the deformed BLEOR images in planes and cylinders.

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