Compression method for original image data

Abstract

The invention provides a compression method for original image data. The method comprises the following steps: dividing and reshaping the original image data according to different colors to obtain four color areas; dividing each color area to obtain N sub-areas composed of rectangles or L shapes; converting numerical values in the sub-areas into expression modes of weighted values and a plurality of differences; using the shapes, difference bit widths, weighted values and all differences of the sub-areas to replace the numerical values in the original sub-areas; and carrying out reverse operation to reduce an original image. The compression method provided by the invention is simple in algorithm and is very low requirements on CPU performance, thus being more suitable for hand-held shooting devices mainly relying on embedded system; the storage speed and the image volume can be increased, and no data loss is generated; the sub-matrixes of a various forms can be used for better dividing similar areas; and the compression method provided by the invention is used for effectively compressing the original image data to not only improve the compression rate, but also save the storage time and space and reduce the cost.

Description

technical field [0001] The invention relates to the technical field of image sensor and digital image processing, in particular to a compression method for original image data. Background technique [0002] In today's image sensor field, the resolution of image sensors is changing with each passing day, and the image resolution and sampling accuracy are increasing rapidly. At present, sensors with more than 6400W pixels have begun to enter the civilian field, and only lossy compression is used on such large-size sensors. The obtained jpeg, pnp and other images are obviously not enough. For professionals, the original raw image data has become an indispensable part. However, the original image data is very large. Taking 6400W pixels and 12bit sampling precision as an example, an image needs to occupy at least 96M bytes. This is for hand-held shooting devices that generally use low-capacity and low-storage media (CF cards, SD cards) Undoubtedly, it has a great impact on imagi...

AI Technical Summary

Problems solved by technology

[0002] In today's image sensor field, the resolution of image sensors is changing with each passing day, and the image resolution and sampling accuracy are increasing rapidly. At present, sensors with more than 6400W pixels have begun to enter the civilian field, and only lossy compression is used on such large-size sensors. The obtained jpeg, pnp and other images are obviously not enough. For professionals, the original raw image data has become an indispensable part

However, the original image data is very large. Taking 6400W pixels and 12bit sampling precision as an example, an image needs to occupy at least 96M bytes. This is for hand-held shooting devices that generally use low-capacity and low-storage media (CF cards, SD cards) Undoubtedly has a great impact on imaging time and storage capacity

[0003] Usually, it is necessary to use compression technology to compress the image data. However, although the mainstream bzip and winrar are lossless compression, the compression rate of the original image data is very low; although JPG, PNP and other image compression algorithms have high compression rates, they are all Lossy compression, can not restore the original data; JPEG2000 is lossless compression, but the algorithm is complex, even a high-performance CPU may not be able to meet hundreds of image operations per second, and JPEG2000 does not support the original bayerraw image data

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