This paper presents a binarymatrix code based on QR Code (Quick Response Code), denoted as CQR Code (Colored Quick
Response Code), and evaluates the effect of JPEG, JPEG2000 and H.264/AVC compression on the decoding process. The
proposed CQR Code has three additional colors (red, green and blue), what enables twice as much storage capacity when
compared to the traditional black and white QR Code. Using the Reed-Solomon error-correcting code, the CQR Code
model has a theoretical correction capability of 38.41%. The goal of this paper is to evaluate the effect that degradations
inserted by common image compression algorithms have on the decoding process. Results show that a successful decoding
process can be achieved for compression rates up to 0.3877 bits/pixel, 0.1093 bits/pixel and 0.3808 bits/pixel for JPEG,
JPEG2000 and H.264/AVC formats, respectively. The algorithm that presents the best performance is the H.264/AVC,
followed by the JPEG2000, and JPEG.
The mixed raster content (MRC) document-compression standard (ITU T.44) specifies a multilayer representation of a document image. The model is very efficient for representing sharp text and graphics over a background. However, its binary selection layer compromises the representation of scanned data and soft edges. Typical segmentation algorithms that split up the document into layers tend to lift letter colors to the foreground, so that soft edge transitions may not fully belong either to the foreground or background layers, causing "halos" around objects that impair compression performance. We present a method that sharpens the document before compression and softens its edges after MRC-based reconstruction. It builds an edge-sharpening map and estimates the original edge softness at the encoder. The generated map and softness parameters are then used to reconstruct the original soft edges at the decoder. An MRC encoding and decoding scheme based on H.264/AVC and JBIG2 has been used. Experimental results show that, for lower bit rates, the proposed pre-/postprocessing method can improve both subjective and objective compression performance over regular MRC.
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