3 research outputs found
Finite state lattice vector quantization for wavelet-based image coding
IEEE International Symposium on Circuits and Systems, Hong Kong, China, 9-12 June 1997It is well known that there exists strong energy correlation between various subbands of a real-world image. A new powerful technique of Finite State Vector Quantization (FSVQ) has been introduced to fully exploit the self-similarity of the image in wavelet domain across different scales. Lattices in RN have considerable structure, and hence, Lattice VQ offers the promise of design simplicity and reduced complexity encoding. The combination of FSVQ and LVQ gives rise to the so-called FSLVQ, which is proved to be successful in exploiting the energy correlation across scales and simple enough in implementation.published_or_final_versio
Vector quantization for efficient coding of upper subbands
This paper examines the application of vector quantization (VQ) to exploit both intra-band and inter-band redundancy in subband coding. The focus here is on the exploitation of inter-band dependency. It is shown that VQ is particularly suitable and effective for coding the upper subbands. Three subband decomposition-based VQ coding schemes are proposed here to exploit the inter-band dependency by making full use of the extra flexibility of VQ approach over scalar quantization. A quadtree-based variable rate VQ (VRVQ) scheme which takes full advantage of the intra-band and inter-band redundancy is first proposed. Then, a more easily implementable alternative based on an efficient block-based edge estimation technique is employed to overcome the implementational barriers of the first scheme. Finally, a predictive VQ scheme formulated in the context of finite state VQ is proposed to further exploit the dependency among different subbands. A VRVQ scheme proposed elsewhere is extended to provide an efficient bit allocation procedure. Simulation results show that these three hybrid techniques have advantages, in terms of peak signal-to-noise ratio (PSNR) and complexity, over other existing subband-VQ approaches
Distortion-constraint compression of three-dimensional CLSM images using image pyramid and vector quantization
The confocal microscopy imaging techniques, which allow optical sectioning, have
been successfully exploited in biomedical studies. Biomedical scientists can benefit
from more realistic visualization and much more accurate diagnosis by processing and
analysing on a three-dimensional image data. The lack of efficient image compression
standards makes such large volumetric image data slow to transfer over limited
bandwidth networks. It also imposes large storage space requirements and high cost in
archiving and maintenance.
Conventional two-dimensional image coders do not take into account inter-frame
correlations in three-dimensional image data. The standard multi-frame coders, like
video coders, although they have good performance in capturing motion information,
are not efficiently designed for coding multiple frames representing a stack of optical
planes of a real object. Therefore a real three-dimensional image compression
approach should be investigated.
Moreover the reconstructed image quality is a very important concern in compressing
medical images, because it could be directly related to the diagnosis accuracy. Most of
the state-of-the-arts methods are based on transform coding, for instance JPEG is based on discrete-cosine-transform CDCT) and JPEG2000 is based on discrete-
wavelet-transform (DWT). However in DCT and DWT methods, the control
of the reconstructed image quality is inconvenient, involving considerable costs in
computation, since they are fundamentally rate-parameterized methods rather than
distortion-parameterized methods. Therefore it is very desirable to develop a
transform-based distortion-parameterized compression method, which is expected to
have high coding performance and also able to conveniently and accurately control
the final distortion according to the user specified quality requirement.
This thesis describes our work in developing a distortion-constraint three-dimensional
image compression approach, using vector quantization techniques combined with
image pyramid structures. We are expecting our method to have:
1. High coding performance in compressing three-dimensional microscopic
image data, compared to the state-of-the-art three-dimensional image coders
and other standardized two-dimensional image coders and video coders.
2. Distortion-control capability, which is a very desirable feature in medical 2. Distortion-control capability, which is a very desirable feature in medical
image compression applications, is superior to the rate-parameterized methods
in achieving a user specified quality requirement.
The result is a three-dimensional image compression method, which has outstanding
compression performance, measured objectively, for volumetric microscopic images.
The distortion-constraint feature, by which users can expect to achieve a target image
quality rather than the compressed file size, offers more flexible control of the
reconstructed image quality than its rate-constraint counterparts in medical image
applications. Additionally, it effectively reduces the artifacts presented in other
approaches at low bit rates and also attenuates noise in the pre-compressed images.
Furthermore, its advantages in progressive transmission and fast decoding make it
suitable for bandwidth limited tele-communications and web-based image browsing
applications