Rate-constrained modular predictive residual vector quantization of digital images

The use of residual (multiple stage) vector quantizer codevectors in a nearest neighbor classifier for direct classification of image pixel data is proposed. This approach combines the successive approximation process generated by theresidual vector quantizer with sequential decision making. This approach potentially has the advantage of making large data base searches for small object or texture recognition in images both computation and memory efficient. Key Words: nearest neighbor classifier, vector quantization 1 INTRODUCTION The technical goals of counter-mine programs require that systems be developed for processing sensor data forautonomous detection and classification of mine-like objects in image data. The autonomous detection task involveslocal image decisions to discriminate between harmless and potentially hazardous subregions of an image. The au-tonomous classification task involves decisions to verify and identify potential threats. Machine-based identificationusing entire images can sometimes be achieved by very sophisticated, and often, computationally demanding algo-rithms. Machine-based classification using only small regions of an image to identify small objects is much simpler.The scope of this paper is restricted to the effective automation of the detection and classification of small objects( objects that are comprised on the order of 10 '

Transform image coding using scalar quantization, pyramid vector quantization (PVQ), and generalized Lloyd algorithm vector quantization (VQ) and subband image coding using scalar quantization and pyramid vector quantization are considered. In a simulation study at low bit rates (0.5 bit/pel) the transform VQ coder generally provided the largest peak-signal-to-noise ratio (PSNR), although the PVQ subband coder was superior for some images. At larger encoding rates (for which, because of complexity, the generalized Lloyd algorithm VQ was not considered) the scalar-quantizer subband coder generally yielded larger PSNR than did the scalar-quantizer transform coder. The PVQ transform and subband coders provided similar values of PSNR, but the subband coded image had less blockiness than the transform-coded image. >

In this paper, we propose novel progressive scalable region of interest (ROI) image compression schemes with rate, distortion, and complexity trade-off based on vector quantization. The proposed schemes are unbalanced in the sense that the decoder has less computational requirements than the encoder. Moreover, the proposed method provides an interactive tool for fast ROI based browsing from image archives. Residual vector quantization (RVQ) equips the encoder with a multi-resolution apparatus which is useful for rate-distortion trade-of. Having all the advantages of RVQ, jointly sub-optimized RVQ provides a distortion-complexity adjustment. Since the bitstreams associated with the earlier stages of the RVQ encoder are more important for the reconstructed image quality than that of the later stages, an unequal error protection scheme, base on Reed-Solomon codes, has been considered to protect the transmitted bitstream against channel noise.

Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. Jointly optimized RVQ (JORVQ) was introduced as an effective design algorithm for minimizing the overall quantization error. Reflected residual vector quantization (RRVQ) was introduced as an alternative design algorithm for RVQ structure with smaller computation burden. RRVQ works by imposing an additional symmetry constraint on the RVQ code book design. Savings in computation was accompanied by increase in distortion. However, it is expected that an RRVQ codebook being structured in nature, will provide lower output entropy. Therefore, we generalize RRVQ to include noiseless entropy coding. The method is referred to as Entropy-Constrained RRVQ (EC-RRVQ). Simulation results show that EC-RRVQ outperforms RRVQ by 4-dB for memoryless Gaussian and Laplacian sources. In addition, for the same synthetic sources, EC-RRVQ provided an improvement over other entropy-constrained designs, such as entropy-constrained JORVQ (EC-JORVQ). The design performed equally well on image data. In comparison with EC-JORVQ, EC-RRVQ is simpler and outperforms the EC-JORVQ.

Digital image coding using vector quantization [VQ] based techniques provides low-bit rates and high quality coded images, at the expenses of intensive computational demands. The computational requirement which is due to the encoding search process had hindered application of VQ to real-time and high quality coding of color images. Reduction of the encoding search complexity through partitioning of a large code book by tree structured vector quantization [TSVQ] in the spatial domain is proposed in this paper. A. real-time TSVQ algorithm for encoding color is developed on Apollo workstation HP 9000/433. This algorithm contributes to the use of a free distortion measure and reduction of code vector dimension. The mean/quantized residual vector quantizer has been used for reducing computational complexity of the encoding search. Running at clock 33MHz, the proposed algorithm is capable of real-time processing of 512x492 pixels per frame with rate 30 frames/second. Application of this algorithm on a training image(lady face) shows the convergence with low distortion rate as shown in Fig.1(a–e).

In this paper we study coding of video sequences at very low bitrates using an entropy-constrained variable block size residual vector quantizer. For a given complexity, it is well understood that structured vector quantizers perform better than unstructured and unconstrained vector quantizers. A combination of structured vector quantizers is used in this paper to encode the video sequences. The proposed codec uses a Discrete Cosine Transform (DCT) based transform vector quantizer and a variable-rate residual vector quantizer in cascade. The transform-VQ captures the low-frequency information using only a small portion of the bit-budget, while the later stage residual VQ captures the high-frequency information using the remaining bits. A strategy to adaptively refine only areas of high activity using recursive decomposition and selective refinement is used in the later stages. An entropy constraint is used to modify the codebooks to allow better entropy coding of the indices. Performance of the proposed codec is evaluated and compared with the H.263 based codec. Experimental results show not only better quantitative performance but also significantly better perceptual quality.

Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named residual vector quantization (RVQ). Next, we propose generalized residual vector quantization (GRVQ) to further improve over RVQ. Many vector quantization methods can be viewed as special cases of our proposed method. To enable GRVQ on billion scale data, we introduce a nonexhaustive search scheme named aggregating tree (A-Tree) for high dimensional data that uses GRVQ encodings to build a radix tree and perform the nearest neighbor search by beam search. To search accurately and efficiently, VQ-encodings should satisfy locally aggregating encoding criterion: For any node of the corresponding A-Tree, neighboring vectors should aggregate in fewer subtrees to make beam search efficient. We show that the proposed GRVQ encodings best satisfy the suggested criterion, and the joint use of GRVQ and A-Tree shows significantly better performances on billion scale datasets. Our methods are validated on several standard benchmark datasets. Experimental results and empirical analysis show the superior efficiency and effectiveness of our proposed methods compared to the state-of-the-art for large scale search.

We have developed novel progressive scalable region-of-interest (ROI) image compression schemes with rate-distortion-complexity tradeoff based on vector quantization. Residual vector quantization (RVQ) equips the encoder with a multi-resolution apparatus which is useful for rate-distortion tradeoff. Having all advantages of RVQ, jointly suboptimized RVQ provides a distortion-complexity adjustment. The systems are unbalanced in the sense that the decoder has less computational requirements than the encoder. The proposed jointly suboptimized RVQ method provides an interactive tool for fast ROI-based browsing from image archives.

Optimal weight assignment for recursive vector quantization (VQ) schemes is investigated. Recursive VQ, such as the predictive VQ and the finite-state VQ, manages to reduce the interblock dependence through effective prediction. Since the prediction is based on previously decoded blocks, the quantization error of the pixels used for estimation will feed forward to subsequent coding blocks. The quantization error of these estimating pixels can be reduced by appropriate weight assignment in the associated distortion measure. We investigate the weight assignment which optimizes the predictive mean-removed VQ. This result can be generalized to other recursive VQ schemes.

Image compression is a basic need for the progressive transmission or storage of image data via any media. For the effective processing of image data the coding standards were developed. The most commonly used standard in current scenario is the JPEG and JPEG-2000 coding. These standards have become the industry standards and are progressively been in use for almost all image processing applications. In the process of image coding various sub-processing operations occur which process the image blocks by transformation, quantization and encoding before transmitting or storing. In the process, quantization is observed to be a time consuming and is needed to be processed in high precision so as to achieve less processing error and also in reasonable time. The process of quantization has shown greater efficiency in vector quantization than earlier methods. Vector quantization is observed to be an efficient block based lossy compression technique. Due to simpler coding process and high compression ratio VQ are widely used. Predictive vector Quantization (PVQ) is commonly used memory based vector Quantization method. The main drawback with PVQ is the high computational complexity which limits PVQ to use in real time applications. Shorter coding time can be archived by reducing the number of code words or instructions, but the reconstructed image quality will degraded in the case of fewer code words. Hence, it is necessary to propose fast coding algorithms for PVQ. In this paper a modified version of the PVQ approach has been suggested in order to process faster, which ensue to be a compatible mechanism for real time image coding in JPEG based coding system. Keywords: Compression, quantization, PVQ encoder, mean-square-error, Discrete Cosine Transform.

In the modern display panel manufacturing industry, the Mura problem that occurs during initial shipment has a significant impact on improving image quality. The purpose of this paper is to effectively solve these Mura defects while reducing additional production costs. We propose an innovative method of storing and compressing the De‐Mura compensation data of the display panel in external memory. Two‐dimensional compensation data compression based on machine learning using surface fitting and vector quantization is presented as an effective response to the Mura problem. This approach, which achieves up to 8:1 compression ratio while minimizing image quality degradation through vector quantization, is expected to provide economic benefits to manufacturers. The paper also introduces a new surface prediction method that replaces the existing DC‐shift and uses residual vector quantization, which can reduce the implementation complexity of vector quantization. Through experimental results, this study demonstrates that the compensated data compression technique yields excellent results in post‐compression restoration performance.

This paper presents a new FSVQ scheme called Finite-State Residual Vector Quantization (FSRVQ) in which each state uses a Residual Vector Quantizer (RVQ) to encode the input vector. Furthermore, a novel tree- structured competitive neural network is proposed to jointly design the next-state and the state-RVQ codebooks for the proposed FSRVQ. Joint optimization of the next-state function and the state-RVQ codebooks eliminates a large number of redundant states in the conventional FSVQ design; consequently, the memory requirements are substantially reduced in the proposed FSRVQ scheme. The proposed FSRVQ can be designed for high bit rates due to its very low memory requirements and low search complexity of the state-RVQs. Simulation results show that the proposed FSRVQ scheme outperforms the conventional FSVQ schemes both in terms of memory requirements and perceptual quality of the reconstructed image. The proposed FSRVQ scheme also outperforms JPEG (current standard for still image compression) at low bit rates.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A new predictive vector quantization (PVQ) technique capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks (vectors) of pixels is introduced. The two components of the PVQ scheme, the vector predictor and the vector quantizer, are implemented by two different classes of neural networks. A multilayer perceptron is used for the predictive cornponent and Kohonen self-organizing feature maps are used to design the codebook for the vector quantizer. The multilayer perceptron uses the nonlinearity condition associated with its processing units to perform a nonlinear vector prediction. The second component of the PVQ scheme vector quantizes the residual vector that is formed by subtracting the output of the perceptron from the original input vector. The joint-optimization task of designing the two components of the PVQ scheme is also achieved. Simulation results are presented for still images with high visual quality.

We present a practical video coding algorithm for use at very low bit rates. For efficient coding at very low bit rates, it is important to intelligently allocate bits within a frame, and so a powerful variable-rate algorithm is required. We use vector quantization to encode the motion-compensated residue signal in an H.263-like framework. For a given complexity, it is well understood that structured vector quantizers perform better than unstructured and unconstrained vector quantizers. A combination of structured vector quantizers is used in our work to encode the video sequences. The proposed codec is a multistage residual vector quantizer, with transform vector quantizers in the initial stages. The transform-VQ captures the low-frequency information, using only a small portion of the bit budget, while the later stage residual VQ captures the high-frequency information, using the remaining bits. We used a strategy to adaptively refine only areas of high activity, using recursive decomposition and selective refinement in the later stages. An entropy constraint was used to modify the codebooks to allow better entropy coding of the indexes. We evaluate the performance of the proposed codec, and compare this data with the performance of the H.263-based codec. Experimental results show that the proposed codec delivered significantly better perceptual quality along with better quantitative performance.

An adaptable joint source/channel coding system based on residual vector quantization (RVQ) is being developed for channels with nonstationary noise characteristics. The system consists of a residual vector quantizer that uses different numbers of stages for different input vectors. The variable rate RVQ used in this adaptable system is designed with a new design process. This design process generates, in general, different encoder and decoder codebooks. The encoder codebooks provide computationally efficient sequential search tree structures, and the decoder codebooks satisfy conditions necessary for joint optimality. >