Image analysis – Image compression or coding – Adaptive coding
Reexamination Certificate
2007-10-30
2007-10-30
Couso, Jose L. (Department: 2624)
Image analysis
Image compression or coding
Adaptive coding
Reexamination Certificate
active
10473494
ABSTRACT:
Activities are normalized corresponding to a characteristic of a pattern of a frame. The picture quality is optimized by adaptive quantization. A normalized activity norm_act [m] of each macro block is obtained with an average activity avg_act of the frame and the activity act [m] of each macro block according to the following formulas (1) and (2). In the following formulas, att is a parameter, for example att=0.125.in-line-formulae description="In-line Formulae" end="lead"?norm_gain=att×avg_act+1 (1)in-line-formulae description="In-line Formulae" end="tail"?in-line-formulae description="In-line Formulae" end="lead"?norm_act[m]={(norm_gain×act[m]×act[m])+(avg_act×avg_act)}÷{(act[m]×act[m])+(norm_gain×avg_act×avg_act)} (2)in-line-formulae description="In-line Formulae" end="tail"?Thus, the normalized activities norm_act [m] are normalized in the range from 1
orm_gain to norm_gain. Since norm_gain is proportional to the average activity avg_act, when a picture has a flat pattern whose average activity avg_act is small, the normalizing range becomes small. Thus, the quantized values of macro blocks are not largely different. As a result, the picture is equally quantized. In contrast, when a picture has a complex pattern whose average activity avg_act is large, the normalizing range becomes large. Thus, the quantized values of macro block are largely different. As a result, a macro block having a flat pattern is finely quantized, whereas a macro block having a complex pattern is coarsely quantized.
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Couso Jose L.
Frommer William S.
Frommer & Lawrence & Haug LLP
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