JPEG 2000 Part 10 is a new work part of the ISO/IEC JPEG Committee dealing with the extension of JPEG 2000 technologies to three-dimensional data. One of the issues in Part 10 is the ability to encode floating point datasets. Many Part 10 use cases come from the scientific and engineering communities, where floating point data is often produced either from numerical simulations or from remote sensing instruments. This paper presents the technologies that are currently being developed to accommodate this Part 10 requirement. The coding of floating point datasets with JPEG 2000 requires two changes to the coding pipeline. Firstly, the wavelet transformation stage is optimized to correctly decorrelate data represented with the IEEE 754 floating point standard. Special IEEE 754 floating point values like Infinities and NaN's are signaled beforehand as they do not correlate well with other floating point values. Secondly, computation of distortion measures on the encoder side is performed in floating point space, rather than in integer space, in order to correctly perform rate allocation. Results will show that these enhancements to the JPEG 2000 coding pipeline lead to better compression results than Part 1 encoding where the floating point data had been retyped as integers.
JPEG 2000 Part 10 is a new work part of the ISO/IEC JPEG Committee dealing with the extension of JPEG 2000 technologies to three-dimensional data. One of the issues in Part 10 is the ability to encode non-uniform data grids having variable resolution across its domain. Some parts of the grid can be more finely sampled than others in accordance with some pre-specified criteria. Of particular interest to the scientific and engineering communities are variable resolution grids resulting from a process of adaptive mesh refinement of the grid cells. This paper presents the technologies that are currently being developed to accommodate this Part 10 requirement. The coding of adaptive mesh refinement grids with JPEG 2000 works as a two step process. In the first pass, the grid is scanned and its refinement structure is entropy coded. In the second pass, the grid samples are wavelet transformed and quantized. The difference with Part 1 is that wavelet transformation must be done over regions of irregular shape. Results will be shown for adaptive refinement grids with cell-centered or corner-centered samples. It will be shown how the Part 10 coding of an adaptive refinement grid is backwards compatible with a Part 1 decoder.
JPEG 2000 is a flexible standard for coding many different types of images. This flexibility will be increased in the near future with the JP3D standard, an extension of JPEG 2000 to three dimensions. This paper presents a set of techniques to code three-dimensional textured parametric surfaces, common in Computer Graphics applications, using JPEG 2000 and possibly using extensions from the newer JP3D standard. These coding techniques generate compressed files of much smaller size than conventional file formats for storing three-dimensional objects. This is because the proposed coding techniques are able to exploit the strong correlation that exists between neighboring surface points in a parametric representation. Furthermore, it is possible to encode parametric surfaces which change through time, something that is not available in many conventional file formats. The reversible compression mode of JPEG 2000 ensures that threedimensional surfaces can be recovered without error after decompression. This is an important requirement for surfaces used in medical imaging, for example. This paper will demonstrate how a large variety of surfaces can be coded with JPEG 2000. The techniques presented here impose only one, but important, constraint on the surfaces: they must obey a parametric representation.
An extension of the JPEG 2000 standard is presented for non-conventional images resulting from an adaptive subdivision process. Samples, generated through adaptive subdivision, can have different sizes, depending on the amount of subdivision that was locally introduced in each region of the image. The subdivision principle allows each individual sample to be recursively subdivided into sets of four progressively smaller samples. Image datasets generated through adaptive subdivision find application in Computational Physics where simulations of natural processes are often performed over adaptive grids. It is also found that compression gains can be achieved for non-natural imagery, like text or graphics, if they first undergo an adaptive subdivision process. The representation of adaptive subdivision images is performed by first coding the subdivision structure into the JPEG 2000 bitstream, ina lossless manner, followed by the entropy coded and quantized transform coefficients. Due to the irregular distribution of sample sizes across the image, the wavelet transform must be applied on irregular image subsets that are nested across all the resolution levels. Using the conventional JPEG 2000 coding standard, adaptive subdivision images would first have to be upsampled to the smallest sample size in order to attain a uniform resolution. The proposed method for coding adaptive subdivision images is shown to perform better than conventional JPEG 2000 for medium to high bitrates.