Formal Photograph Compression Algorithm Based on Object Segmentation

Small storage space for photographs in formal documents is increasingly necessary in today＇s needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs. That is, the object is an image of the human head, and the background is in unicolor. Therefore, the compression is of low efficiency and the image after compression is still space-consuming. This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications. To achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background. Two methods, lossless image contour coding based on differential chain, and modified set partitioning in hierarchical trees （SPIHT） algorithm of arbitrary shape, are discussed in detail. The results of experiments show that when bit per pixel （bpp）is equal to 0.078, peak signal-to-noise ratio （PSNR） of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.

A Lossy Contour-Based Representation of Segmentation Maps Using Quadratic Transformations Motion Prediction

In the context of object oriented video coding, the encoding of segmentation maps defined by contour networks is particularly critical. In this paper, we present a lossy contour network encoding algorithm where both the rate distortion contour encoding based on maximum operator and the prediction error for the current frame based on quadratic motion model are combined into a optimal polygon contour network compression scheme. The bit rate for the contour network can be further reduced by about 20% in comparison with that in the optimal polygonal boundary encoding scheme using maximum operator in the rate distortion sense.