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This chapter addresses the family of progressive image compression algorithms.
Used OpenCV in Python on Raspberry Pi (Raspian) to implement object recognition and image compression algorithms.
Alex will be starting his new job as a research scientist at WaveOne on July 2nd, where he will be creating context-adaptive image compression algorithms.
Image compression algorithms based on wavelet transform can achieve ideal effect on optical image, while cannot do well in SAR image compression.
To achieve high compression efficiency, the main image compression algorithms used in wireless sensor networks are the high-complexity, state-of-the-art image compression standards, such as JPEG2000.
In the experiments, we show that on a set of benchmark images our approach has found filter banks that perform better than the existing filter banks in different image compression algorithms and at different compression ratios.
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This paper considers the design of a lossy image compression algorithm dedicated to color still images.
This chapter describes the process of mapping the image compression algorithm SPIHT onto reconfigurable logic architecture.
This article presents a lightweight image compression algorithm explicitly designed for resource-constrained wireless camera sensors, called TiBS (tiny block-size image coding).
The proposed implementation scheme of the image compression algorithm overcomes the computation and energy limitation of individual nodes by sharing the processing of tasks.
In contrast to existing methods that design filter banks independently from the other operations in an image compression algorithm, our approach allows us to find filter banks that work best in a specific image compression algorithm for a certain class of images.
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