Unequal error protected ROI image transmission based on vector quantization [region of interest compression scheme

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.

In view of increasing use of video transmission over wireless channels, a lot of research efforts have been made to trade-off the error resiliency and transmission bandwidth requirements. This in turn has led to development of various unequal error protection (UEP) schemes. In this paper, the performance of optimized 2-layered and 3-layered UEP schemes using hierarchical 64 QAM (64-HQAM) for wavelet-transformed images coded with set partitioning in hierarchical trees (SPIHT) algorithm and transmitted over additive white Gaussian noise (AWGN) channel is investigated. A look-up table (LUT) based approach to select the optimal values of modulation parameters of 64-HQAM for different channel conditions is suggested so as to reduce the computational complexity. Simulation results show that 3-layered optimized 64-HQAM-based UEP outperforms corresponding non-optimized UEP. Also, 3-layered optimized UEP scheme using 64-HQAM performs slightly better as compared to optimized 2-layered UEP scheme using 64-HQAM.

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 '

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.

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.

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).

This paper presents a new unequal error protection (UEP) image transmission system that incorporates a Lifting Wavelet Transform (LWT) and Reed Solomon (RS) coded cooperation scheme to increase image transmission diversity, as well as save transmission bandwidth. Having a partner to assist direct communication increases the resilience of low frequency subband data against an error-prone fading channel. Low frequency subbands are partitioned into two sets of data and transmitted using the RS coded cooperation scheme. High frequency subbands data are transmitted directly to a base station. Results show that the new UEP image transmission system using LWT based RS coded cooperation scheme achieves diversity gains of around 10dB, with channel SNR from 10 to 20dB, compared with the image transmission system with non-cooperative system under slow Rayleigh fading channel for all levels of LWT decomposition. In addition, the new UEP image transmission system using LWT based RS coded cooperation scheme with one level of wavelet decomposition offers around 37.5% bandwidth gain (ß), compared with the system without LWT, which incurs a reduction of 3dB in reconstructed image quality.

In this paper we propose an image coding scheme based on Block Turbo Codes (BTC) for protecting SPIHT coded images transmitted over wireless channels. An Unequal Error Protection (UEP) scheme using different product codes is also presented. The optimization of the UEP scheme under a transmission budget constraint is investigated. Performance of the proposed system are first evaluated over Gilbert-Elliott (GE) channels. They are then compared to the well-known system of Sherwood and Zeger, through numerical simulations over a single path Rayleigh fading channel. Then, we present an optimized mobile image transmission framework involving a multiuser asynchronous DS-CDMA transmission system over Rayleigh multipath channels. Simulation results for the 512x512 Lena image, transmitted over the ITU Vehicular A channel, show that a significant gain in PSNR is obtained by using the SIC/RAKE multiuser detector in comparison with a conventional detection by the RAKE receiver. Furthermore, performance are also improved when introducing the UEP scheme.

We present a dynamic unequal error protection (UEP) framework for the embedded image bit streams over noisy channels. A general source model for M-rate UEP schemes is derived and an optimization algorithm for 2-rate dynamic UEP schemes is developed. To facilitate the design and implementation of UEP schemes, we also derived a necessary condition and an upper bound for UEP gains. Simulation results demonstrate about 0.3 dB improvements over equal error protection (EEP) schemes at the price of negligible overheads while the progressiveness of the original bit streams is still kept.

A novel modular coding paradigm is investigated using residual vector quantization (RVQ) with memory that incorporates a modular neural network vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks that are optimized for predicting a particular class of data. The predictor also consists of an integrating unit that mixes the outputs of the expert networks to form the final output of the prediction system. The vector quantizer also has a modular structure. The proposed modular predictive RVQ (modular PRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same peak signal-to-noise ratio (PSNR), the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.

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.

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.

We develop and test a new, two-stage, residual vector quantization algorithm using variable bit-rate encoding. In the first stage, we partition the input image into non-overlapping blocks, vector-quantize and code them by a small codebook using the well-known K-means algorithm. The novelty in this method is the use of high eigen-valued blocks as initial seeds which serve as good distributors in the formation of clusters and fast convergence. We compute the residual vectors and classify them based on threshold values of distortion and variance. Vectors above the given threshold require second-stage coding. In the second stage, we partition the residual vectors further into small sub blocks and scalar-quantize each sub block to form number patterns instead of performing Direct Vector Quantization (DVQ). These number patterns, which form the secondary codebook, are easily generated without complex calculations by applying basic ideas from combinatorics. Both the intra-block and inter-block correlation properties have been exploited to enhance the compression rate. This method offers several advantages: 1) the computational complexity is greatly reduced; 2) exhaustive comparisons in DVQ are carried out more efficiently; 3) the picture quality of the reconstructed image is not compromised; and, 4) a reduced bit-rate is achieved.

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. >

In real-world scenarios, speech signals are inevitably corrupted by various types of interference, making speech enhancement (SE) a critical task for robust speech processing. However, most existing SE methods only handle a limited range of distortions, such as additive noise, reverberation, or band limitation, while the study of SE under multiple simultaneous distortions remains limited. This gap affects the generalization and practical usability of SE methods in real-world environments. To address this gap, this paper proposes a novel Universal Discrete-domain SE model called UDSE. UDSE primarily works to enhance speech by predicting clean discrete tokens that are quantized by the residual vector quantizer (RVQ) of a pre-trained neural speech codec and are predicted in accordance with the RVQ rules. Specifically, UDSE first extracts global features from the degraded speech. Guided by these global features, the clean token prediction for each VQ follows the rules of RVQ, where the prediction of each VQ relies on the results of the preceding ones. Finally, the predicted clean tokens from all VQs are decoded to reconstruct the clean speech waveform. During training, the UDSE model employs a teacher-forcing strategy, and is optimized with cross-entropy loss. Experimental results confirm that the proposed UDSE model can effectively enhance speech degraded by various conventional and unconventional distortions, e.g., additive noise, reverberation, band limitation, clipping, phase distortion, and compression distortion, as well as their combinations. These results demonstrate the superior universality and practicality of UDSE compared to advanced regression-based SE methods.