Quantization (image processing)

About: Quantization (image processing) is a research topic. Over the lifetime, 7977 publications have been published within this topic receiving 126632 citations.

Perceptual feedback in multigrid motion estimation using an improved DCT quantization

Abstract: In this paper, a multigrid motion compensation video coder based on the current human visual system (HVS) contrast discrimination models is proposed. A novel procedure for the encoding of the prediction errors has been used. This procedure restricts the maximum perceptual distortion in each transform coefficient. This subjective redundancy removal procedure includes the amplitude nonlinearities and some temporal features of human perception. A perceptually weighted control of the adaptive motion estimation algorithm has also been derived from this model. Perceptual feedback in motion estimation ensures a perceptual balance between the motion estimation effort and the redundancy removal process. The results show that this feedback induces a scale-dependent refinement strategy that gives rise to more robust and meaningful motion estimation, which may facilitate higher level sequence interpretation. Perceptually meaningful distortion measures and the reconstructed frames show the subjective improvements of the proposed scheme versus an H.263 scheme with unweighted motion estimation and MPEG-like quantization.

Combining low-frequency and spread-spectrum watermarking

Abstract: Low-frequency watermarks and watermarks generated using spread spectrum techniques have complementary robustness properties. In this paper, we combine both watermarking paradigms to design an oblivious watermark that is capable of surviving an extremely wide range of severe image distortions. An image is first watermarked with a low- frequency pattern and then a spread spectrum signal is added to the watermarked image. Since both watermarks are embedded in a different portion of the frequency space, they do not interfere. For the low-frequency watermark, we modify the NEC scheme so that the original image is not needed for watermark extraction. The image is first normalized nd the watermark is embedded into the lowest frequency discrete cosine modes by encoding a binary pattern using a special quantization-like function. The low-frequency watermark is combined with a spread spectrum signal added to the middle frequencies of a DCT. The resulting double watermarked image is extremely robust with respect to a very wide range of quite severe image distortions including low-pass filtering, pixel permutations, JPEG compression, noise adding, and nonlinear deformations of the signal, such as gamma correction, histogram manipulations, and dithering.

Digital image watermarking based on DWT-DCT: Evaluate for a new embedding algorithm

Abstract: The authenticity of content or matter is crucial factors for solving the problem of copying, modifying, and distributing the intellectual properties in an illegal way. Watermarking can resolve the stealing problem of intellectual properties. This paper considers a robust image watermarking technique based on discrete wavelet transform (WDT) and discrete cosine transform (DCT) called hybrid watermarking. The hybrid watermarking is performed by two level, three level, and four level DWT followed by respective DCT on the host image. A new embedding algorithm (NEA) of digital watermarking is proposed in this paper. The simulation results are compared with Cox's additive embedding algorithm and the NEA for additive white Gaussian noise (AWGN) attack and without attack. Both algorithms use the hybrid watermarking. The NEA gives 3.04dB and 9.33dB better pick signal to noise ratio (PSNR) compared to Cox's additive algorithm for the 4 level DWT for AWGN attack and without attack, respectively. Moreover, the NEA extracts the marked image 46 times better of Cox's additive algorithm in 2 level DWT with AWGN attack. That means, the NEA can embed larger marks and high quality marks extract from the embedded watermarking even attacking condition. Though the NEA is evaluated in this paper by comparing performances with Cox's algorithm, the performances of NEA will compare among other algorithms like Gaussian sequence, image fusion, nonlinear quantization embedding with various attacking conditions in near future.

Low intensity contouring and color shift reduction using dither

Abstract: A method and system for reducing the effects of false contouring and reducing color shading artifacts. An image signal 102 is dithered by the addition of a small noise signal from a noise generator 500. The added noise signal breaks up the edges of homogenous blocks of pixels, causing the created image to appear to have a smooth transition from one region to the next. The image dithering is especially useful in digital color image displays where processing performed on the chrominance portion of the image signal often causes quantization errors which lead to sharp transitions between similar shades when the input image included a smooth transition.

Efficient and Secure Image Communication System Based on Compressed Sensing for IoT Monitoring Applications

Abstract: The Internet of Things (IoT) has attracted extensive attention in the information field. Its rapid development has promoted several monitoring application domains. However, the resource constraint of sensor nodes and the security of data transmission have emerged as significant issues. In this paper, an image communication system for IoT monitoring applications is exploited to solve the above-mentioned problems simultaneously. The proposed system can satisfy the requirements of sensor nodes for low computational complexity, low-energy consumption, and low storage overhead. We also present a new compressed sensing (CS) model, as well as the corresponding parallel reconstruction algorithm, which help to reduce the image encryption/decryption time. Based on chaotic systems, we integrate the quantization and diffusion operations into the system to further enhance the transmission security. The simulations are executed to demonstrate the feasibility and the effectiveness of the proposed method. Compared with the traditional CS, our numerical results indicate that the proposed model reduces 413 ms computation time and 3.13 × 10 6 elements stored for large-scale images. Besides, we verify the flexibility and the diversity of choosing two submatrices for different-sized images. Experimental results also show the proposed system performs well in terms of security performance. Particularly the key space reaches 2253.