Tae-Sun Choi

A heuristic approach for finding best focused shape

The most popular shape from focus (SFF) methods in the literature are based on the concept of focused image surface (FIS)-the surface formed by the best focus points. According to paraxial-geometric optics, there is one-to-one correspondence between the shape of an object and the shape of its FIS. Therefore, the problem of three-dimensional (3-D) shape recovery from image focus can be described as the problem of determining the shape of the FIS. The conventional SFF method is inaccurate because of piecewise constant approximation of the FIS. The SFF method based on the FIS has shown better results by exhaustive search of the FIS shape using planar surface approximation at the cost of considerably higher computations. In this paper, search of the FIS shape is presented as an optimization problem, i.e., maximization of the focus measure in the 3-D image volume. The proposed method searches the optimal focus measure in the whole image volume, instead of the small volume as adopted in previous methods. The dynamic programming, instead of the approximation techniques, is used to search the optimal FIS shape. A direct application of dynamic programming on a 3-D data is impractical, because of higher computational complexity. Therefore a fast heuristic model based on dynamic programming is proposed for the search of FIS shape. The shape recovery results of the new method are better than previous methods. The proposed algorithm is significantly faster than the FIS algorithm, but a little slower than the conventional algorithm.

Shape From Focus Using Optimization Technique

Three dimensional (3D) shape recovery using shape from focus (SFP) is presented as an optimization problem, i.e., maximization of the focus measure in the 3D image volume. The whole image volume (sequence) is divided into a number of sub image volumes. The search of 3D shape is made in the sub image volumes using dynamic programming optimization technique. The final depth map is obtained by collecting the depth map of the sub volumes. The new algorithm has considerably decreased the computational complexity by searching the 3D shape in sub image volumes and has shown better results. New 3D focus measure operators are also introduced for higher accuracy at the cost of some computational costs

Linked significant tree wavelet-based image compression

This paper proposes a new linked significant tree (LST) wavelet coding method for improved compression of images. The proposed method first links all the significant coefficients together within a wavelet tree to facilitate encoding algorithm. Insignificant parents, with at least one significant child found in the wavelet data, are changed to significant to link them together. Their location is saved for identification, and they are then treated just like any other significant coefficients during encoding. An embedded LST encoding algorithm is proposed to accommodate the processed output for better coding efficiency. It exploits the fact that no descendent of an insignificant coefficient can be significant any more, since all the significant coefficients within a wavelet tree are linked together. This fact eliminates the need to check the descendents of those coefficients that are found as insignificant. Locations of converted coordinates are sent to the decoder after each encoding pass, to change the values back to original during reconstruction. The proposed method shows improvement when applied to gray-scale images.

Embedded Linked Significant Tree Wavelet Image Coder

This paper presents a new linked significant tree (LST) wavelet coding method for efficient compression of images. The proposed method links all the significant coefficients together within a wavelet tree to facilitate encoding algorithm. Insignificant parents with at least one significant child found in the wavelet data are changed to significant to link them together. Their location is saved for identification, and they are then treated just like other significant coefficients during encoding. LST encoding process is performed next on the processed output for compression. It exploits the fact that since all the significant coefficients within a wavelet tree are linked together, no descendent of an isolated coefficient can be significant any more. This eliminates the need to check descendents of those coefficients that are found as insignificant. Locations of converted coordinates are sent to the decoder after each encoding pass, in order to change these values back to original during reconstruction. Better compression is achieved when the proposed method is applied to smooth images

Processing of wavelet transform data for improved image compression

This paper presents a novel preprocessing technique of wavelet transform data to link all the significant coefficients together, in order to facilitate the image coding algorithms for increased compression. The proposed algorithm finds the isolated significant coefficients in the wavelet transformed data for current threshold value. All the insignificant coefficients in the wavelet tree that lies above that isolated significant coefficient (i.e., its insignificant parents) are changed to significant coefficients and their location is saved, they are then treated just like other significant coefficients. Coding algorithms have been modified to accommodate the processed output. In the end, algorithm indicates converted coordinates that are used to change the value back to original during reconstruction. Noticeably high compression ratio is achieved for most of the images, when the proposed method is used with the modified image codecs.

Simplified EZW image coder with residual data transmission

Wavelet based compression is becoming popular due to its promising compaction properties at low bit rate. A zerotree wavelet image coding scheme efficiently exploits multi-level redundancy present in transformed data to minimize coding bits. In this paper, a new technique is proposed to achieve high compression by adding new zerotree and significant symbols to the original EZW coder. Contrary to four symbols present in the basic EZW scheme, the modified algorithm uses eight symbols to generate fewer bits for the given data. A subordinate pass of EZW is eliminated and replaced with fixed residual value transmission for easy implementation. This modification simplifies the coding technique as well and speeds up the process, retaining the property of embeddedness.

Video data reduction with error resilience based on macroblock reorder

We present a new technique to improve the video compression ratio. In this technique, macroblock data are reordered in such a way that one block includes the important data of a macroblock, while the other three data blocks hold difference values in the horizontal, vertical, and diagonal directions. This results in reduced bit stream size because of low-valued data in the three blocks, giving a higher compression ratio. The proposed method can be easily used for error resilience applications as well. In that case, the important data block in a macroblock is transmitted on a secure channel while the remaining three blocks with difference data are sent via a lossy channel. In the case of an error in the lossy channel, the picture can still be reconstructed with a reasonably good quality using the block that contains important data transmitted on the secure channel. The proposed method generates better reconstruction quality when used at low bit rates.

Enhanced Video Coding with Error Resilience Based on Macroblock Data Manipulation

With the rapid growth of video traffic, interest in the coding of video data has increased. Two new techniques are presented to significantly improve video compression ratio, with marginal effect on reconstructed quality. In both these techniques, important data of a macroblock is compressed in one block, while rest of the three data blocks hold difference values in horizontal, vertical and diagonal direction. This results in reduced bitstream size because of low valued data in the three blocks, giving higher compression ratio. These algorithms have an additional advantage that they can be effectively used for error resilience applications with good error handling capacity. For error resilient applications, important data block in a macroblock is transmitted in a secure channel and the remaining three blocks with difference data are sent via lossy channel. In case of error in lossy channel, picture can still be reconstructed with a reasonably good quality using the block transmitted in secure channel that contains important data. Better reconstruction quality is obtained after compression, when used at low bitrates.

Highly efficient zerotree wavelet image Coder with reduced symbols

This paper presents a novel wavelet comrpession technique to increase compression of images. Based on extension of zerotree entoropy coding method, this method initially uses only two symbols (significant and zerotree) to compress image data for each level. Additionally, sign bit is used for newly significant coefficients to indicate them being positive or negative. Contrary to isolated-zero symbols used in conventional zerotree algorithms, the proposed algorithm changes them to significant coefficients and saves its location, they are then treated just like other significant coefficients. This is done to decrease the number of symbols and hence decrease number of bits to represent the symbols used. In the end, algorithm indicates isolated-zero coordinates that are used to change the value back to original during reconstruction. Noticeably high compression ratio is achieved with no change in image quality.

Wavelet-based Image Compression using Subband Threshold

Wavelet based image compression has been a focus of research in recent days. In this paper, we propose a compression technique based on modification of original EZW coding. In this lossy technique, we try to discard less significant information in the image data in order to achieve further compression with minimal effect on output image quality. The algorithm calculates weight of each subband and finds the subband with minimum weight in every level. This minimum weight subband in each level, that contributes least effect during image reconstruction, undergoes a threshold process to eliminate low-valued data in it. Zerotree coding is done next on the resultant output for compression. Different values of threshold were applied during experiment to see the effect on compression ratio and reconstructed image quality. The proposed method results in further increase in compression ratio with negligible loss in image quality.