<title>Block implementation of arithmetic coding for image compression</title>

Abstract

The Full Frame Discrete Cosine Transform (FFDCT) compression method has the capability to eliminate block artifacts that exist in JPEG and MPEG compressed images. Previous FFDCT algorithm used an adaptive Bit Allocation (BA) method to pack quantized DCT coefficients. In this work, we replaced BA with Arithmetic Coding (AC) to improve compression performance markedly. We will also show that by partitioning the quantized cosine domain much higher arithmetic coding efficiency can be achieved, especially in highly quantized images. . INTRODUCTIONHigh ratio, transform based compression is critically needed to make archiving and telecommunication of digital medical images feasible. The most widely supported compression methods are JPEG and MPEG standards, both based on the block DCT approach that introduces block artifacts in the reconstructed images. Because diagnostic reading is particularly sensitive to artifacts, such methods may not be acceptable in quality. Recent study has shown that even when block artifacts are not immediately obvious, they become prominent after edge enhancement[ 1]. Many studies have introduced modifications to the basic block DCT method in an effort to minimize discontinuities across blocks [2,3,4,5]. However, only two methods completely remove them. They are the wavelet transform coding which has recently gained a great deal of attention, and the more established Full Frame DCT method.Previously, we introduced the FFDCT algorithm along with a custom hardware module to accelerate DCT computation[6,7,81. The HVS based quantization table used by JPEG was rejected in favor of a threshold based quantization scheme because we believed that high frequency components often contain crucial diagnostic detail and should not be deemphasized as is done in JPEG. The quantized coefficients were encoded using an adaptive bit allocation method, which is particularly suited for parallel processing. However, this method necessarily includes spurious zero bits which offer no information, and generates a bit allocation table that represents a sizable overhead in highly compressed images. In this paper, we replaced the bit allocation step with arithmetic coding that better exploits the entropy inherent in the quantized coefficients. It will be shown that not only is the overall compression efficiency increased, the parallel processing property is still preserved. FFDCT AND HARDWARE MODULEDiscrete Cosine Transform (DCT) is excellent for image compression because of its energy compacting properties. In order to ensure complete elimination of block artifacts, we shunned the common practice of partitioning an image into small blocks before doing cosine transform. Performing DCT in full frame has the added burden of high computational complexity, which we addressed using an efficient DCT implementation based on a modified FFT[9]. We also took a different approach in deriving the quantization levels since sharp edges cany significant diagnostic information (interstitials, calcifications) and must be preserved with eq...

<title>Video compression algorithm for dynamic angiographic images</title>

“…Finally, each subblocks of quantized DWT coefficients is independently bit-packed by arithmetical coding (AC) [20J. Block AC isolates the distinct image models in each subblock to arrive at low average data entropy and therefore achieving a lower bit rate than would be possible if the entire DWT domain is arithmetically coded using a single model [21].…”