Hyper spectral video transcoding is the operation of converting a video from one format into another format. A format is defined by such characteristics as the bit rate, frame rate, spatial resolution, coding syntax, and content. Due to significantly improved spatial and spectral resolution provided by a hyper spectral imaging sensor, hyper spectral imagery expands capability of multispectral imagery in many. For example, a TV program may be originally compressed at a high bit rate for studio applications, but later needs to be transmitted over a channel at a much lower bit rate.

Hyper spectral video are widely used in a number of civilian and military applications. The images are acquired from plane or satellite borne spectrometers and cover large tracts of the Earth’s surface. Through the analysis of the spectrum of reflected light present in these images, it is possible to identify what materials are present on the land and in the atmosphere. This information has been used for such varied purposes as environmental studies, military surveillance and the analysis and location of mineral deposits.


When data compression is performed, two different types of criteria must be specified. One is a design criterion used to develop a compression technique and the other is a performance criterion used to evaluate the effectiveness of a specified compression technique in performance. While these two types of criteria are considered as separate criteria, they are generally correlated to each other. Specifically, a performance criterion is always a major driving force to determine what design criterion should be selected to design a desired compression technique.

Despite the fact that a hyper spectral video can be viewed as a 3D image cube, there are several major unique features that a hyper spectral image distinguishes itself from being viewed as a 3D image cube. The first and foremost is spectral features provided by hundreds of contiguous spectral channels. Unlike pure vowels’ in a 3D image, a hyper spectral image pixel vector is specified by a range of wavelengths in a third dimension that characterizes the spectral properties of a single pixel vector. Using the spectral profile captured in the spectral domain a single pixel in a 3D image cube can be solely analyzed by its spectral characterization. Another important unique feature provided by hyper spectral imagery is that many material substances of interest can be only explored by their spectral properties, not spatial properties such as waste in environmental pollution, chemical/biological agent detection in bioterrorism, camouflaged combat vehicles and decoys in surveillance applications. In addition, certain targets such as chemical plumes, biological agents, which are considered to be relatively small with no rigid shapes but yet provide significant information, generally cannot be identified by prior knowledge. Instead, these targets can be only uncovered and revealed by their spectral properties. Therefore, when a compression ratio is high, whether or not a hyper spectral image compression technique is effective may not be necessarily determined by its spatial compression as do most compression techniques in image processing since small and subtle targets such as sub pixel and mixed pixel targets may be very likely sacrificed by low-bit rate compression due to their limited spatial presence.

Representative APR 391%

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