In a standard transform coding scheme of images or video, the decoder can be implemented by a table-lookup technique without the explicit use of an inverse transformation, In this new decoding method, each received code index of a transform coefficient addresses a particular codebook to fetch a component code vector that resembles the basis vector of the linear transformation. The output image is then reconstructed by summing a small number of nonzero component code vectors. With a set of well-designed codebooks, this new decoder can exploit the correlation among the quantized transform coefficients to achieve better rate-distortion performance than the conventional decoding method. An iterative algorithm for designing a set of locally optimal codebooks from a training set of images is presented. We demonstrate that this new idea can be applied to decode improved quality pictures from the bitstream generated from a standard encoding scheme of still images or video, while the complexity is low enough to justify practical implementation.

A low-complexity iterative algorithm is introduced for joint estimation of forward and backward motion vectors in interpolative prediction of video. Starting from the initial values obtained by a commonly-used block matching independent search method, the motion vectors are iteratively refined until a locally optimal solution to the motion estimation problem for interpolative prediction is achieved. Each iteration consists of a series of two similar procedures. First, the backward motion vector is fixed and a new forward motion vector is searched to minimize the interpolation error. Then the forward motion vector is fixed and the backward motion vector is similarly refined by minimizing the interpolation error. This process is repeated until the interpolation error stops decreasing. Computer simulation results demonstrate that with this technique the prediction error in some scenes is significantly reduced.

Transform coding, a simple yet efficient image coding technique, has been adopted by the Joint Photographic Experts Group (JPEG) as the basis for an emerging coding standard for compression of still images. However, for any given transform encoder, the conventional inverse transform decoder is suboptimal. Better performance can be obtained by a nonlinear interpolative decoder that performs table lookups to reconstruct the image blocks from the code indexes. Each received code index of an image block addresses a particular codebook to fetch a component vector. The image block can be reconstructed as the sum of the component vectors for that block. An iterative algorithm for designing a set of locally optimal codebooks is developed. Computer simulation results demonstrate that this improved decoding technique can be applied in the JPEG baseline system to decode enhanced quality pictures from the bit stream generated by the standard encoding scheme.>

An image coding algorithm for digital video tape recorders (DVTR) must satisfy several requirements which do not arise in other applications of video compression. A key constraint on the data format is satisfied if every frame (or field) of video is partitioned into a small number of subimages, each independently coded with a fixed number of bits. This requirement excludes the use of interframe coding and most variable-rate image coding algorithms. We propose to use a new algorithm that codes a subimage efficiently under this data format constraint and allows virtually lossless reproduction at reasonable low bit rates. Each subimage is partitioned into non-overlapping blocks and each block is coded by one of a finite set of predesigned block quantizers covering a range of bit rates. For the ith block in the subimage, a rate function Ri(Li) and a distortion function Di(Li) is tabulated for each block quantizer Li. A near-optimal quantizer allocation algorithm based on the Lagrange Multiplier method is used to select a particular quantizer for each block. The objective is to minimize the distortion of the entire subimage under the constraint on the total number of bits for the subimage. A system for the compression of HDTV for DVTR's based on this rate-constrained optimal block-adaptive technique is designed using the DCT and vector quantization with a multistage structure. Simulation results demonstrate that this algorithm has the potential of achieving virtually lossless compression for digital tape recording of HDTV.