Quadtree Segmentation Research Articles

Adaptive DCT for image‐data compression

AbstractThis paper deals with the development of a coding system for image data compression based on the Discrete Cosine Transform (DCT). The aim is to integrate several methods into a single approach in order to obtain an adaptive coder characterized by higher performances. In particular, a variable blocksize scheme is used, which is based on quadtree segmentation driven by edge information. To improve data compression, DCT coefficients are then coded by a vector quantizer.Experiments have shown that the values of compression factors can be notably increased, while maintaining a good reconstruction quality.

Interframe hierarchical address-vector quantization

A new interframe coding technique called interframe hierarchical address-vector quantization (IHA-VQ) is presented. It exploits the local characteristics of a moving image's motion-compensated (via block matching) difference signals by using quadtree segmentation to divide each image into large, uniform regions and smaller, highly detailed regions. The detailed regions are encoded by vector quantization (VQ), and the larger, low-detail regions are replenished from the previous frame, IHA-VQ also exploits the correlation between the small blocks by encoding the addresses of neighboring vectors by using several codebooks of address code-vectors. >

Hierarchical block truncation coding

We present a new image coding technique called hierarchical block truncation coding (HBTC). HBTC is a combination of the block truncation coding (BTC) technique and the quadtree segmentation method. Quadtree segmentation is used to decompose an image into homogeneous regions so that the BTC method can exploit the nonstationary characteristics of the image data. The resulting bit rate is lower than that of conventional BTC, depending on the characteristics and complexity of the digital image. We investigated the performance of the encoder on both still and moving images. A small reduction in the bit rate is achievable for still images, but false contours become apparent as the rate declines. The proposed method works well on difference images from a sequence since the moving areas are encoded with greater resolution than the stationary background. A significant bit rate reduction is seen for sequence transmission. The bit rate is reduced from the 1.625 bits per pixel (bpp) required for a conventional BTC implementation down to 1.19 bpp for the least detailed still image. A typical CCITT image sequence was encoded at an average rate of 1.21 bpp. The bit rate was further reduced to an average of 0.39 bpp with a small degradation in the quality of the reconstructed images by transmitting only the most varying portions of the sequence.

Hierarchical block truncation coding of digital HDTV images

A source coding technique for compressing HDTV (high-definition television) digital images for B-ISDN (broadband integrated services digital network) visual services is presented. The proposed technique is called hierarchical block truncation coding (HBTC). It is a combination of the block truncation coding (BTC) technique and a quadtree segmentation method. The quadtree segmentation is used to decompose an image into homogeneous regions so that the BTC method can exploit the nonstationarity of the image data. Bit rates around 1.46 b/pixel are achieved with this method. Experimental results for several HDTV images are presented. It is shown that greater image compression can be achieved by using a hierarchical BTC method in conjunction with quadtree segmentation, than by applying BTC to an image with uniform block divisions, since the correlation of uniform areas is exploited. This is especially true for appropriate images such as HDTV with large uniform areas.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Quadtree-structured linear prediction models for image sequence processing

A summary is presented of a study on two-dimensional linear prediction models for image sequence processing and its application to change detection and scene coding. The study focused on two-dimensional joint process modeling of interframe relationships, the derivation of computationally efficient matching algorithms, and the implementation of a block-adaptive interframe predictor for use in interframe predictive coding and change detection. In the approach presented, the spatial nonstationarity is handled by an underlying quadtree segmentation structure. A maximum-likelihood criterion and a simpler minimum-variance criterion are discussed as detection and segmentation rules. The results of this research indicate that a constrained joint process model involving only a single gain parameter and a shift parameter is the best tradeoff between performance and computational complexity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>