This paper proposes the design and implementation of a real-time image compressor using 2-Dimensional Discrete Wavelet Transform (2DDWT), which targets an FPGA as its platform. The image compressor uses Daubechies' bi-orthogonal DWT filters (9, 7) and 16-bit fixed-point data formats for wavelet coefficients in the internal calculation. The target image is NTSC 640240 pixels per field whose color format is Y:Cb:Cr = 4:2:2. We developed for the 2DDWT a new structure with four Multipliers and Accumulators (MACs) for real-time operations. We designed and used a linear fixed scalar quantizer, which includes the exceptional treatment of the coefficients whose absolute values are larger than the quantization region. Only a Huffman entropy encoder was included due to the hardware overhead. The quantizer and Huffman encoder merged into a single functional module. Due to the insufficient memory space of an FPGA, we utilized external memory (SDRAM) as the working and memory storage space. The proposed image compressor maps into an APEX20KC EP20K600CB652-7 from Altera and uses 45% of the Logic Array Block (LAB) and 9% of the Embedded System Block (ESB). With a 33 MHz clock frequency, the proposed image compressor shows a speed of 67 fields per second (33 frames per second), which is more than real-time operation. The resulting image quality from reconstruction is approximately 28 dB in PSNR and its compression ratio is 29:1. Consequently, the proposed image compressor is expected to be used in a dedicated system requiring an image-processing unit.

In this paper, we proposed an efficient coding method for digital hologram (fringe pattern) acquired with a CCD camera or by computer generation using multi-view prediction and MPEG video compression standard techniques. It processes each R, G, or B color component separately. The basic processing unit is a partial image segmented as the size of MN. Each partial image retains the information of the whole object. This method generates an assembled image for a column of the segmented and frequency-transformed partial images, which is the basis of the coding process. That is, a motion estimation and compensation technique of MPEG is applied between the reconstructed images from the assembled images with the disparities found during generation of assembled image and the original partial images. Therefore the compressed results are the disparity of each partial image to form the assembled image for the corresponding column, assembled image, and the motion vectors and the compensated image for each partial image. The experimental results with the implemented algorithm showed that the proposed method has NC (Normalized Correlation) values about 4% higher than the previous method at the same compression ratios, which convinced us that ours has better compression efficiency. Consequently, the proposed method is expected to be used effectively in the application areas to transmit or store in digital format the digital hologram data.

In this paper, we propose a wavelet-based fast motion estimation algorithm for video sequence encoding with a low bit-rate. By using one of the properties of wavelet transform, multi-resolution analysis (MRA), and the spatial interpolation of an image, we can simultaneously reduce the prediction error and the computational complexity inherent in video sequence encoding. In addition, by defining a significant block (SB) based on the differential information of wavelet coefficients between successive frames, the proposed algorithm enables us to make up for the increase in the number of motion vectors when the MRME algorithm is used. As a result, we are not only able to improve the peak signal-to-noise ratio (PSNR), but also reduce the computational complexity by up to 67%.