acm - an acm publication

Tinku Acharya Collection

  • Computational foundations of image interpolation algorithms

    Image interpolation is an important image processing operation applied in diverse areas ranging from computer graphics, rendering, editing, medical image reconstruction, to online image viewing. Image interpolation techniques are referred in literature by many terminologies, such as image resizing, image resampling, digital zooming, image magnification or enhancement, etc. Basically, an image interpolation algorithm is used to convert an image from one resolution (dimension) to another resolution without loosing the visual content in the picture. Image interpolation algorithms can be grouped in two categories, non-adaptive and adaptive. The computational logic of an adaptive image interpolation technique is mostly dependent upon the intrinsic image features and contents of the input image whereas computational logic of a non-adaptive image interpolation technique is fixed irrespective of the input image features. In this paper, we review the progress of both non-adaptive and adaptive image interpolation techniques. We also proposed a new algorithm for image interpolation in discrete wavelet transform domain and shown its efficacy. We describe the underlying computational foundations of all these algorithms and their implementation techniques. We present some experimental results to show the impact of these algorithms in terms of image quality metrics and computational requirements for implementation.

  • VLSI Algorithms and architectures for JPEG2000
    In this paper, we presented VLSI algorithms and architectures for JPEG2000. JPEG2000 is the new standard for image compression. We briefly described the core algorithms for JPEG2000 standard and its several features desirable in many interactive multimedia applications. The core compression algorithm has three major components - discrete wavelet transform (DWT), fractional bit plane coding (BPC), and context adaptive binary arithmetic coding. DWT and BPC are very computationally, as well as memory expensive operations. As a result, special purpose VLSI implementations of these algorithms are desirable for many devices to compress large size images in real time. Traditionally, the DWT is realized by convolution based finite impulse response (FIR) filtering techniques. However, Lifting based implementation of DWT is found to be computationally efficient and suitable for VLSI implementations. The basic principle behind the lifting based scheme is to decompose the finite impulse response filters in wavelet transform into a finite sequence of simple filtering steps. Lifting based DWT implementation have been recommended in JPEG2000 standard. Consequently, this has become an area of active research and several architectures have been proposed in recent years. In this paper, we reviewed some of the key lifting architectures suitable for VLSI implementation. The embedded block coding with optimized truncation (EBCOT) algorithm has been adopted for computation of BPC in JPEG2000. This algorithm is complex and inefficient to implement in a general purpose machine. We have described a special purpose architecture suitable for VLSI implementation of EBCOT algorithm. We also reviewed some elegant architectures in the literature which exploit the underlying data and computational parallelism inherent in the EBCOT algorithms. We also presented a top-level systems architecture for VLSI implementation of the JPEG2000 standard. ...