Predictive Residual Vector Quantization Research Articles

Rate-constrained modular predictive residual vector quantization of digital images

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.

Entropy‐constrained predictive residual vector quantization

A major problem with a vector-quantization-based image compression scheme is its codebook search complexity. Recently, a new vector quantization (VQ) scheme called the predictive residual vector quantizer (PRVQ) was proposed, which gives performance very close to that of the predictive vector quantizer (PVQ) with very low search complexity. This paper presents a new variable-rate VQ scheme called the entropy-constrained PRVQ (EC-PRVQ), which is designed by imposing a constraint on the output entropy of the PRVQ. We emphasized the design of the EC-PRVQ for bit rates ranging from 0.2 to 1.00 bits per pixel. This corresponds to compression ratios of 8 through 40, which is the range likely to be used by most of the real-life applications permitting Iossy compression. The proposed EC-PRVQ is found to give a good rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithm developed by the Joint Photographic Experts Group (JPEG). The robustness of the EC-PRVQ is demonstrated by encoding several test images taken from outside the training data. The EC-PRVQ not only gives better performance than JPEG, at a manageable encoder complexity, but also retains the inherent simplicity of a VQ decoder.

Variable-rate predictive residual vector quantizer

A major problem with a VQ-based image compression scheme is its codebook search complexity. Recently, a predictive residual vector quantizer (PRVQ) was proposed by Rizvi and Nasrabadi (see IEEE Int. Conf. Image Processing, Austin, vol.1, p.608-612, Nov. 13-16, 1994). This scheme has a very low search complexity, and its performance is very close to that of the predictive vector quantizer (PVQ). The article presents a new VQ scheme called variable-rate PRVQ (VR-PRVQ), which is designed by imposing a constraint on the output entropy of the PRVQ. The proposed VR-PRVQ is found to give an excellent rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithm developed by the Joint Photographic Experts Group (JPEG). >

Predictive residual vector quantization [image coding

This paper presents a new vector quantization technique called predictive residual vector quantization (PRVQ). It combines the concepts of predictive vector quantization (PVQ) and residual vector quantization (RVQ) to implement a high performance VQ scheme with low search complexity. The proposed PRVQ consists of a vector predictor, designed by a multilayer perceptron, and an RVQ that is designed by a multilayer competitive neural network. A major task in our proposed PRVQ design is the joint optimization of the vector predictor and the RVQ codebooks. In order to achieve this, a new design based on the neural network learning algorithm is introduced. This technique is basically a nonlinear constrained optimization where each constituent component of the PRVQ scheme is optimized by minimizing an appropriate stage error function with a constraint on the overall error. This technique makes use of a Lagrangian formulation and iteratively solves a Lagrangian error function to obtain a locally optimal solution. This approach is then compared to a jointly designed and a closed-loop design approach. In the jointly designed approach, the predictor and quantizers are jointly optimized by minimizing only the overall error. In the closed-loop design, however, a predictor is first implemented; then the stage quantizers are optimized for this predictor in a stage-by-stage fashion. Simulation results show that the proposed PRVQ scheme outperforms the equivalent RVQ (operating at the same bit rate) and the unconstrained VQ by 2 and 1.7 dB, respectively. Furthermore, the proposed PRVQ outperforms the PVQ in the rate-distortion sense with significantly lower codebook search complexity.