Showing papers by "Gemma Piella published in 2006"

Combining Seminorms in Adaptive Lifting Schemes and Applications to Image Analysis and Compression

Abstract: In this paper, we present some adaptive wavelet decompositions that can capture the directional nature of images. Our method exploits the properties of seminorms to build lifting structures able to choose between different update filters, the choice being triggered by the local gradient-type features of the input. In order to deal with the variety and wealth of images, one has to be able to use multiple criteria, giving rise to multiple choice of update filters. We establish the conditions under these decisions can be recovered at synthesis, without the need for transmitting overhead information. Thus, we are able to design invertible and non-redundant schemes that discriminate between different geometrical information to efficiently represent images for lossless compression methods.

Modified M-band synthesis filter bank for fractional scalability of images

Abstract: Subband image coding with classical wavelet transforms inherently provides dyadic scalability since the low-pass approximation band can be used as a half-sized version of the original image The multiresolution approach further provides scalability factors that are powers of two, but no other factors can be easily reached On the other hand, a hierarchical scheme based on M-bandtransforms will lead only to scalability factors that are powers of M In this letter, we present how an M-band synthesis filter bank can be modified in order to directly reconstruct an output image of a resolution reduced by a factor M/P, where P is an integer lower than M As the analysis filter bank is not modified, this technique allows the design of subband decoding schemes that support fractional scalability With this feature, the same compressed bitstream can be directly decoded by different receivers at several resolutions, depending on their computational and bandwith capabilities

Adaptive wavelets for image compression using update lifting: quantization and error analysis

Abstract: Classical linear wavelet representations of images have the drawback that they are not optimally suited to represent edge information To overcome this problem, nonlinear multiresolution decompositions have been designed to take into account the characteristics of the input signal/image In our previous work20,22,23 we have introduced an adaptive lifting framework, that does not require bookkeeping but has the property that it processes edges and homogeneous image regions in a different fashion The current paper discusses the effects of quantization in such an adaptive wavelet decomposition We provide conditions for recovering the original decisions at the synthesis and for relating the reconstruction error to the quantization error Such an analysis is essential for the application of these adaptive decompositions in image compression

Image interpolation using an adaptive invertible approach

Abstract: In this paper we present a new image interpolation algorithm based on the adaptive update lifting scheme described in [1]. This scheme allows us to build adaptive wavelets that take into account the characteristics of the underlying signal. Inspired by this technique, we propose an image resizing scheme which has the ability to adapt itself to discontinuities like edges and assures a perfect reconstruction going from low to high resolution and then back to low resolution. Such a feature is highly desirable, for example, for forth and back conversion between the two existing High Definition Television formats, in order to preserve the integrity of the original image in a chain of successive transformations. The proposed algorithm adaptively updates one polyphase component of the original image and then computes the rest of the components of the output image by means of a gradient-driven interpolation method.