Search

Although halftone image watermarking technologies have been rapidly developing in the 21st century, the existing techniques lack a theoretical basis. In this paper, we tackle halftone image watermarking problems from a theoretical perspective. First, we propose a general optimization framework for Halftone Visual Watermarking (HVW), which is a certain category of halftone image watermarking techniques. Then two specific HVW problems, Single-sided Embedding Error Diffusion (SEED) and Double-sided Embedding Error Diffusion (DEED) are presented and solved by applying the proposed framework. With SEED and DEED obtained, both the theoretical solutions and experimental results indicate that our previous heuristic methods, Data Hiding by Conjugate Error Diffusion (DHCED) and Data Hiding by Dual Conjugate Error Diffusion (DHDCED), are special cases of SEED and DEED, respectively. We also demonstrate that DEED can achieve outstanding performance compared to DHDCED and other previous methods by selecting different parameters. With this paper, we essentially build a bridge between the theory and practical implementations of HVW problems. © 2015 Elsevier B.V. All rights reserved.

Provided is sub-block motion estimation that includes a full search with a reduced search area around a motion vector predictor. Also provided are a single (or unified) motion vector predictor and a function similar to a rate-distortion cost function. Also provided is a scanning order that can reduce or substantially eliminated redundant data loading and/or that can achieve different data re-use ratio. Further, provided herein is a reconfigurable multi-resolution motion vector re-use hardware architecture based on a single motion vector predictor and a function similar to a rate-distortion cost function. Further, through utilization of the disclosed aspects, only a small number of past motion vectors are propagated and the disclosed aspects can be reconfigured for video with different spatial resolution.

Provided is sub-block motion estimation that includes a full search with a reduced search area around a motion vector predictor. Also provided are a single (or unified) motion vector predictor and a function similar to a rate-distortion cost function. Also provided is a scanning order that can reduce or substantially eliminated redundant data loading and/or that can achieve different data re-use ratio. Further, provided herein is a reconfigurable multi-resolution motion vector re-use hardware architecture based on a single motion vector predictor and a function similar to a rate-distortion cost function. Further, through utilization of the disclosed aspects, only a small number of past motion vectors are propagated and the disclosed aspects can be reconfigured for video with different spatial resolution.

Although halftone image watermarking technologies have been rapidly developing in the 21st century, the existing techniques lack a theoretical basis. In this paper, we tackle halftone image watermarking problems from a theoretical perspective. First, we propose a general optimization framework for Halftone Visual Watermarking (HVW), which is a certain category of halftone image watermarking techniques. Then two specific HVW problems, Single-sided Embedding Error Diffusion (SEED) and Double-sided Embedding Error Diffusion (DEED) are presented and solved by applying the proposed framework. With SEED and DEED obtained, both the theoretical solutions and experimental results indicate that our previous heuristic methods, Data Hiding by Conjugate Error Diffusion (DHCED) and Data Hiding by Dual Conjugate Error Diffusion (DHDCED), are special cases of SEED and DEED, respectively. We also demonstrate that DEED can achieve outstanding performance compared to DHDCED and other previous methods by selecting different parameters. With this paper, we essentially build a bridge between the theory and practical implementations of HVW problems. © 2015 Elsevier B.V. All rights reserved.

Provided is sub-block motion estimation that includes a full search with a reduced search area around a motion vector predictor. Also provided are a single (or unified) motion vector predictor and a function similar to a rate-distortion cost function. Also provided is a scanning order that can reduce or substantially eliminated redundant data loading and/or that can achieve different data re-use ratio. Further, provided herein is a reconfigurable multi-resolution motion vector re-use hardware architecture based on a single motion vector predictor and a function similar to a rate-distortion cost function. Further, through utilization of the disclosed aspects, only a small number of past motion vectors are propagated and the disclosed aspects can be reconfigured for video with different spatial resolution.

In this paper, an adaptive block coding order for intra prediction is proposed. Modern video coding standards, including the most recent High Efficiency Video Coding (HEVC), utilize fixed scan orders in processing blocks during intra coding. However, the fixed scan orders typically result in residual blocks with noticeable edge patterns. That means the fixed scan orders cannot fully exploit the content-adaptive spatial correlations between adjacent blocks, thus the bitrate after compression tends to be large. To reduce the bitrate induced by inaccurate intra prediction, the proposed approach adaptively chooses both the block and subblock coding orders by minimizing the coding cost. Specifically, determining the block coding order is formulated as a traveling salesman problem that is solved using dynamic programming. Besides the block coding order, we also design the subblock coding order in each block with an adaptive manner. The experimental results demonstrate a Bjontegaard-Delta-rate reduction of up to 4.4% compared with HEVC anchor.

Although halftone image watermarking technologies have been rapidly developing in the 21st century, the existing techniques lack a theoretical basis. In this paper, we tackle halftone image watermarking problems from a theoretical perspective. First, we propose a general optimization framework for Halftone Visual Watermarking (HVW), which is a certain category of halftone image watermarking techniques. Then two specific HVW problems, Single-sided Embedding Error Diffusion (SEED) and Double-sided Embedding Error Diffusion (DEED) are presented and solved by applying the proposed framework. With SEED and DEED obtained, both the theoretical solutions and experimental results indicate that our previous heuristic methods, Data Hiding by Conjugate Error Diffusion (DHCED) and Data Hiding by Dual Conjugate Error Diffusion (DHDCED), are special cases of SEED and DEED, respectively. We also demonstrate that DEED can achieve outstanding performance compared to DHDCED and other previous methods by selecting different parameters. With this paper, we essentially build a bridge between the theory and practical implementations of HVW problems.

This paper proposes a novel reversible image data hiding scheme over encrypted domain. Data embedding is achieved through a public key modulation mechanism, in which access to the secret encryption key is not needed. At the decoder side, a powerful two-class SVM classifier is designed to distinguish encrypted and nonencrypted image patches, allowing us to jointly decode the embedded message and the original image signal. Compared with the state-of-the-art methods, the proposed approach provides higher embedding capacity and is able to perfectly reconstruct the original image as well as the embedded message. Extensive experimental results are provided to validate the superior performance of our scheme.

We present a novel framework for high bit-precision image acquisition and reconstruction. This framework is designed based on the inherent Markov property of image signals. In acquisition stage, we add planned sensor distortion (PSD) to the analog image signal before feeding it to A/D converters (or quantizers) in camera sensor. In reconstruction stage, the acquired quantized pixel values are jointly combined to get the reconstructed signal with reduced uncertainty range. Advantages of proposed PSD framework include 1) simplicity: it does not require any change to the core hardware of existing A/D converters; 2) effectiveness: experiment results demonstrate significant PSNR gain over traditional methods (up to 10 dB when quantizer bit-depth is relatively low); and 3) generality: this framework can also be applied for acquisition of other analog signals, including audio, video, etc. © 2014 IEEE.

The High Efficiency Video Coding (HEVC) utilizes Z-scan order to process coding units (CUs). For intra prediction, this order cannot fully exploit the spatial correlation between adjacent CUs. After transform and quantization, the residue still contains lots of energy along edges which consumes many bits for compression. To effectively reduce the residue energy along edges, a novel intra prediction approach is proposed, where the CU processing order is changed adaptively. Two additional orders are introduced in this paper besides traditional Z-scan order. Up to 1.9% bit saving is achieved in our experiments on HEVC test model. We also propose two fast order selection algorithms and the observed gains are obtained with 27% and 2% encoding time increase compared to HEVC, respectively.

Digital image matting is the determination of foreground color, background color, and an opacity value of each pixel for an input image. Inherently, matting is a highly ill-posed and under-constrained problem. Thus, some assumptions need to be made to resolve it. Inspired by closed-form matting and color clustering matting, in this work, we first develop an adaptive sample clustering criterion to automatically assign either local or nonlocal neighborhood to each pixel. After that, in order to enhance matting accuracy, we improve the nonlocal clustering performance by introducing a new feature selection parameter to choose preferred feature space for different images in a fully automatic way. And finally we solve the problem using a closed form solution. Experimental results show that our algorithm achieves equal or even better performance among many state-of-the-art matting techniques.

In this work, we tackle the problem of coloring black-and-white images, which is image colorization. Existing image colorization algorithms can be categorized into two types: scribble-based colorization algorithms and example-based colorization algorithms. Differently, we propose a hybrid scheme that combines the advantages of both categories. Given the grayscale image to be colorized and a few color scribbles (or scattered color labels) as input, the proposed method manages to colorize the grayscale image with high quality. Similar to the mechanisms in example-based colorization methods, our algorithm firstly propagates chrominance information based on the assumption that similar image patches should have similar colors. Therefore colors of some pixels can be transferred from similar patches with known colors. After that, we apply scribble-based colorization algorithm to fully colorize the grayscale image, with different confidences assigned onto the transferred color labels. Experimental results show that, the proposed method effectively utilizes the known chrominance, and provides pleasant colorizations with very few user interventions. © 2014 IEEE.

In this paper, we propose a novel image compression approach towards visual quality rather than pixel fidelity. We intentionally remove several blocks at the encoder and reconstruct them at the decoder to get bits reduction. The removal blocks are wisely and adaptively selected based on blocks clustering, patch similarity and removal priority. A well-suited similarity measurement is defined to capture the common pattern between patches as well as tell their substitutability based on boundary consistency. To assist the removal blocks reconstruction at the decoder, we extract some dense descriptors as the side information to the decoder. Encouraging experimental results show that our compression scheme achieves up to 20.26%bits reduction with a comparable visual quality compared to the most recent standard High Efficiency Video Coding (HEVC).

Motion estimation (ME) serves as a key tool in a variety of video coding standards. With the increasing need for higher resolution video format, the limited memory bandwidth becomes a bottleneck for ME implementation. The huge data loading from external memory to the on-chip memory and the frequent data fetching from the on-chip memory to the ME engine are two major problems. To reduce both off-chip and on-chip memory bandwidth, we propose a two-layer data reuse scheme. On the macroblock (MB) layer, an advanced Level C data reuse scheme is presented. It employs two cooperating onchip caches which load data in a novel local-snake scanning manner. On the block layer, we propose a fast variable block size motion estimation with a refined search window (RSW-VBSME). A new approach for hardware implementation of VBSME is then employed based on the fast algorithm. Instead of obtain the SADs of all the modes at the same time, the ME of different block sizes are performed separately. This enables higher data reusability within an MB. The two-layer data reuse scheme archives a more than 90% reduction of off-chip memory bandwidth with a slight increase of on-chip memory size. Moreover, the on-chip memory bandwidth is also greatly reduced compared with other reuse methods with different VBSME implementations.

Image colorization aims to add colors to grayscale images, which used to be a time-consuming and tedious task that requires lots of human efforts. In this paper, we present a novel colorization method based on color propagation and rank minimization. Given a small portion of chrominance values and a grayscale image, we firstly propagate the known color values to other pixels to be colorized. As the colorized image after color propagation is not accurate, we then define a confidence matrix to measure the propagation fidelity. Finally, pixels that have propagated chrominance values with confidence are colorized by rank minimization, which exploits the redundancy of natural images. Experimental results on real data set show that our proposed method achieves state-of-the-art colorization quality. © 2015 IEEE.

Motion vector fields (MVFs) are able to produce a more accurate prediction image than conventional block based motion compensation. However, MVFs are not used in conventional video coding standards due to the difficulty of efficient estimation and compression. In this work, we propose an MVF based video coding framework. We formulate the estimation of the MVF as a discrete optimization problem by both optimizing the residual energy and MVF smoothness, which can be efficiently solved by a graph cut algorithm with initialized motion vectors for each pixel. We then propose a modified rate distortion optimization approach for the MVF compression. Experimental results show that the proposed method has comparable performance in terms of object quality compared to the state-of-art of HEVC, while it has a better subjective performance by overcoming the block artifacts problem. © 2015 IEEE.

Error Diffusion has been widely adopted in the printing industry due to its good visual quality and simple implementation. However, error diffusion still possesses its own deficiencies. Thus multiscale error diffusion (MED) has been developed, and this method outperforms traditional error diffusion according to extensive research results. The majority of previous halftone image watermarking techniques cannot be directly applied to MED halftone images. Since there is no halftone visual watermarking (HVW) method for MED halftone images in existing methods, the authors propose the first HVW method for MED halftone images, Copyright Protecting Multiscale Error Diffusion (CoP-MED), in this paper. By adopting the visual cryptography principle, CoP-MED can effectively embed a secret pattern into two MED halftone images, where the secret pattern can be decoded clearly by simply overlaying the two stego halftone images or performing not-exclusive-or operation between them. Parameter selection is also discussed based on the experimental results. Later, in comparison tests, CoP-MED shows superior performance compared to existing works. Copyright © 2015, IGI Global.

A method and a computer-readable medium containing computer program for simultaneously performing rate control and video denoising for video corrupted by noise are presented. Video data includes original video data and noise data. A variance of the noise data for each macroblock within a frame is estimated. Based on the estimated noise variance, a low bound quantization parameter is generated. The video data are reconstructed such that the distortion between the original video data and reconstructed video data is minimized over each macroblock within a frame. The minimization of the distortion between the original video and reconstructed video data is implemented using a quantization parameter that is equal to or larger than the low bound quantization parameter.

Motion estimation (ME) serves as a key tool in a variety of video coding standards. With the increasing need for higher resolution video format, the limited memory bandwidth becomes a bottleneck for ME implementation. The huge data loading from external memory to the on-chip memory and the frequent data fetching from the on-chip memory to the ME engine are two major problems. To reduce both off-chip and on-chip memory bandwidth, we propose a two-layer data reuse scheme. On the macroblock (MB) layer, an advanced Level C data reuse scheme is presented. It employs two cooperating onchip caches which load data in a novel local-snake scanning manner. On the block layer, we propose a fast variable block size motion estimation with a refined search window (RSW-VBSME). A new approach for hardware implementation of VBSME is then employed based on the fast algorithm. Instead of obtain the SADs of all the modes at the same time, the ME of different block sizes are performed separately. This enables higher data reusability within an MB. The two-layer data reuse scheme archives a more than 90% reduction of off-chip memory bandwidth with a slight increase of on-chip memory size. Moreover, the on-chip memory bandwidth is also greatly reduced compared with other reuse methods with different VBSME implementations.

In this work, we tackle the problem of coloring black-and-white images, which is image colorization. Existing image colorization algorithms can be categorized into two types: scribble-based colorization algorithms and example-based colorization algorithms. Differently, we propose a hybrid scheme that combines the advantages of both categories. Given the grayscale image to be colorized and a few color scribbles (or scattered color labels) as input, the proposed method manages to colorize the grayscale image with high quality. Similar to the mechanisms in example-based colorization methods, our algorithm firstly propagates chrominance information based on the assumption that similar image patches should have similar colors. Therefore colors of some pixels can be transferred from similar patches with known colors. After that, we apply scribble-based colorization algorithm to fully colorize the grayscale image, with different confidences assigned onto the transferred color labels. Experimental results show that, the proposed method effectively utilizes the known chrominance, and provides pleasant colorizations with very few user interventions. © 2014 IEEE.

We present a novel framework for high bit-precision image acquisition and reconstruction. This framework is designed based on the inherent Markov property of image signals. In acquisition stage, we add planned sensor distortion (PSD) to the analog image signal before feeding it to A/D converters (or quantizers) in camera sensor. In reconstruction stage, the acquired quantized pixel values are jointly combined to get the reconstructed signal with reduced uncertainty range. Advantages of proposed PSD framework include 1) simplicity: it does not require any change to the core hardware of existing A/D converters; 2) effectiveness: experiment results demonstrate significant PSNR gain over traditional methods (up to 10 dB when quantizer bit-depth is relatively low); and 3) generality: this framework can also be applied for acquisition of other analog signals, including audio, video, etc. © 2014 IEEE.

Digital image matting is the determination of foreground color, background color, and an opacity value of each pixel for an input image. Inherently, matting is a highly ill-posed and under-constrained problem. Thus, some assumptions need to be made to resolve it. Inspired by closed-form matting and color clustering matting, in this work, we first develop an adaptive sample clustering criterion to automatically assign either local or nonlocal neighborhood to each pixel. After that, in order to enhance matting accuracy, we improve the nonlocal clustering performance by introducing a new feature selection parameter to choose preferred feature space for different images in a fully automatic way. And finally we solve the problem using a closed form solution. Experimental results show that our algorithm achieves equal or even better performance among many state-of-the-art matting techniques.

The High Efficiency Video Coding (HEVC) utilizes Z-scan order to process coding units (CUs). For intra prediction, this order cannot fully exploit the spatial correlation between adjacent CUs. After transform and quantization, the residue still contains lots of energy along edges which consumes many bits for compression. To effectively reduce the residue energy along edges, a novel intra prediction approach is proposed, where the CU processing order is changed adaptively. Two additional orders are introduced in this paper besides traditional Z-scan order. Up to 1.9% bit saving is achieved in our experiments on HEVC test model. We also propose two fast order selection algorithms and the observed gains are obtained with 27% and 2% encoding time increase compared to HEVC, respectively.

Motion vector fields (MVFs) are able to produce a more accurate prediction image than conventional block based motion compensation. However, MVFs are not used in conventional video coding standards due to the difficulty of efficient estimation and compression. In this work, we propose an MVF based video coding framework. We formulate the estimation of the MVF as a discrete optimization problem by both optimizing the residual energy and MVF smoothness, which can be efficiently solved by a graph cut algorithm with initialized motion vectors for each pixel. We then propose a modified rate distortion optimization approach for the MVF compression. Experimental results show that the proposed method has comparable performance in terms of object quality compared to the state-of-art of HEVC, while it has a better subjective performance by overcoming the block artifacts problem. © 2015 IEEE.

Homomorphic encryption becomes one of the key components in many emerging applications, e.g., cloud computing, to achieve privacy-preserving data processing. However, one of the major drawbacks that precludes the widespread adoption of homomorphic encryption is the huge expansion of the ciphertext. This problem becomes even more severe when multimedia data (images/videos) are handled, as these files are essentially of large sizes. This work addresses this challenging issue and proposes a strategy of reducing the ciphertext expansion in image homomorphic encryption. To this end, a randomly selected subset of the pixels are encrypted using homomorphic cryptosystem to form one part of the ciphertext. The remaining pixels are encrypted by relating them with this random subset through a linear interpolation technique. The whole homomorphically encrypted image can be obtained, upon receiving all the ciphertexts, by exploiting the homomorphic property and the linearity. It is demonstrated that the proposed scheme is secure, and is capable of achieving significant reduction of the ciphertext expansion, while perfectly preserving the homomorphic property. © 2015 IEEE.

In this paper, we propose a novel image compression approach towards visual quality rather than pixel fidelity. We intentionally remove several blocks at the encoder and reconstruct them at the decoder to get bits reduction. The removal blocks are wisely and adaptively selected based on blocks clustering, patch similarity and removal priority. A well-suited similarity measurement is defined to capture the common pattern between patches as well as tell their substitutability based on boundary consistency. To assist the removal blocks reconstruction at the decoder, we extract some dense descriptors as the side information to the decoder. Encouraging experimental results show that our compression scheme achieves up to 20.26%bits reduction with a comparable visual quality compared to the most recent standard High Efficiency Video Coding (HEVC).

Error Diffusion has been widely adopted in the printing industry due to its good visual quality and simple implementation. However, error diffusion still possesses its own deficiencies. Thus multiscale error diffusion (MED) has been developed, and this method outperforms traditional error diffusion according to extensive research results. The majority of previous halftone image watermarking techniques cannot be directly applied to MED halftone images. Since there is no halftone visual watermarking (HVW) method for MED halftone images in existing methods, the authors propose the first HVW method for MED halftone images, Copyright Protecting Multiscale Error Diffusion (CoP-MED), in this paper. By adopting the visual cryptography principle, CoP-MED can effectively embed a secret pattern into two MED halftone images, where the secret pattern can be decoded clearly by simply overlaying the two stego halftone images or performing not-exclusive-or operation between them. Parameter selection is also discussed based on the experimental results. Later, in comparison tests, CoP-MED shows superior performance compared to existing works. Copyright © 2015, IGI Global.

Image colorization aims to add colors to grayscale images, which used to be a time-consuming and tedious task that requires lots of human efforts. In this paper, we present a novel colorization method based on color propagation and rank minimization. Given a small portion of chrominance values and a grayscale image, we firstly propagate the known color values to other pixels to be colorized. As the colorized image after color propagation is not accurate, we then define a confidence matrix to measure the propagation fidelity. Finally, pixels that have propagated chrominance values with confidence are colorized by rank minimization, which exploits the redundancy of natural images. Experimental results on real data set show that our proposed method achieves state-of-the-art colorization quality. © 2015 IEEE.

Motion estimation (ME) serves as a key tool in a variety of video coding standards. With the increasing need for higher resolution video format, the limited memory bandwidth becomes a bottleneck for ME implementation. The huge data loading from external memory to the on-chip memory and the frequent data fetching from the on-chip memory to the ME engine are two major problems. To reduce both off-chip and on-chip memory bandwidth, we propose a two-layer data reuse scheme. On the macroblock (MB) layer, an advanced Level C data reuse scheme is presented. It employs two cooperating onchip caches which load data in a novel local-snake scanning manner. On the block layer, we propose a fast variable block size motion estimation with a refined search window (RSW-VBSME). A new approach for hardware implementation of VBSME is then employed based on the fast algorithm. Instead of obtain the SADs of all the modes at the same time, the ME of different block sizes are performed separately. This enables higher data reusability within an MB. The two-layer data reuse scheme archives a more than 90% reduction of off-chip memory bandwidth with a slight increase of on-chip memory size. Moreover, the on-chip memory bandwidth is also greatly reduced compared with other reuse methods with different VBSME implementations.

A method and a computer-readable medium containing computer program for simultaneously performing rate control and video denoising for video corrupted by noise are presented. Video data includes original video data and noise data. A variance of the noise data for each macroblock within a frame is estimated. Based on the estimated noise variance, a low bound quantization parameter is generated. The video data are reconstructed such that the distortion between the original video data and reconstructed video data is minimized over each macroblock within a frame. The minimization of the distortion between the original video and reconstructed video data is implemented using a quantization parameter that is equal to or larger than the low bound quantization parameter.

We present a novel framework for high bit-precision image acquisition and reconstruction. This framework is designed based on the inherent Markov property of image signals. In acquisition stage, we add planned sensor distortion (PSD) to the analog image signal before feeding it to A/D converters (or quantizers) in camera sensor. In reconstruction stage, the acquired quantized pixel values are jointly combined to get the reconstructed signal with reduced uncertainty range. Advantages of proposed PSD framework include 1) simplicity: it does not require any change to the core hardware of existing A/D converters; 2) effectiveness: experiment results demonstrate significant PSNR gain over traditional methods (up to 10 dB when quantizer bit-depth is relatively low); and 3) generality: this framework can also be applied for acquisition of other analog signals, including audio, video, etc. © 2014 IEEE.

In this work, we tackle the problem of coloring black-and-white images, which is image colorization. Existing image colorization algorithms can be categorized into two types: scribble-based colorization algorithms and example-based colorization algorithms. Differently, we propose a hybrid scheme that combines the advantages of both categories. Given the grayscale image to be colorized and a few color scribbles (or scattered color labels) as input, the proposed method manages to colorize the grayscale image with high quality. Similar to the mechanisms in example-based colorization methods, our algorithm firstly propagates chrominance information based on the assumption that similar image patches should have similar colors. Therefore colors of some pixels can be transferred from similar patches with known colors. After that, we apply scribble-based colorization algorithm to fully colorize the grayscale image, with different confidences assigned onto the transferred color labels. Experimental results show that, the proposed method effectively utilizes the known chrominance, and provides pleasant colorizations with very few user interventions. © 2014 IEEE.

The High Efficiency Video Coding (HEVC) utilizes Z-scan order to process coding units (CUs). For intra prediction, this order cannot fully exploit the spatial correlation between adjacent CUs. After transform and quantization, the residue still contains lots of energy along edges which consumes many bits for compression. To effectively reduce the residue energy along edges, a novel intra prediction approach is proposed, where the CU processing order is changed adaptively. Two additional orders are introduced in this paper besides traditional Z-scan order. Up to 1.9% bit saving is achieved in our experiments on HEVC test model. We also propose two fast order selection algorithms and the observed gains are obtained with 27% and 2% encoding time increase compared to HEVC, respectively.

Digital image matting is the determination of foreground color, background color, and an opacity value of each pixel for an input image. Inherently, matting is a highly ill-posed and under-constrained problem. Thus, some assumptions need to be made to resolve it. Inspired by closed-form matting and color clustering matting, in this work, we first develop an adaptive sample clustering criterion to automatically assign either local or nonlocal neighborhood to each pixel. After that, in order to enhance matting accuracy, we improve the nonlocal clustering performance by introducing a new feature selection parameter to choose preferred feature space for different images in a fully automatic way. And finally we solve the problem using a closed form solution. Experimental results show that our algorithm achieves equal or even better performance among many state-of-the-art matting techniques.

In this paper, we propose a novel image compression approach towards visual quality rather than pixel fidelity. We intentionally remove several blocks at the encoder and reconstruct them at the decoder to get bits reduction. The removal blocks are wisely and adaptively selected based on blocks clustering, patch similarity and removal priority. A well-suited similarity measurement is defined to capture the common pattern between patches as well as tell their substitutability based on boundary consistency. To assist the removal blocks reconstruction at the decoder, we extract some dense descriptors as the side information to the decoder. Encouraging experimental results show that our compression scheme achieves up to 20.26%bits reduction with a comparable visual quality compared to the most recent standard High Efficiency Video Coding (HEVC).

Homomorphic encryption becomes one of the key components in many emerging applications, e.g., cloud computing, to achieve privacy-preserving data processing. However, one of the major drawbacks that precludes the widespread adoption of homomorphic encryption is the huge expansion of the ciphertext. This problem becomes even more severe when multimedia data (images/videos) are handled, as these files are essentially of large sizes. This work addresses this challenging issue and proposes a strategy of reducing the ciphertext expansion in image homomorphic encryption. To this end, a randomly selected subset of the pixels are encrypted using homomorphic cryptosystem to form one part of the ciphertext. The remaining pixels are encrypted by relating them with this random subset through a linear interpolation technique. The whole homomorphically encrypted image can be obtained, upon receiving all the ciphertexts, by exploiting the homomorphic property and the linearity. It is demonstrated that the proposed scheme is secure, and is capable of achieving significant reduction of the ciphertext expansion, while perfectly preserving the homomorphic property. © 2015 IEEE.

Block matching based motion estimation algorithms are adopted in numerous practical video processing applications due to their low complexity. However, conventional block matching based methods process each block independently to minimize the energy function, which results in a local minimum. It fails to preserve the motion details. In this paper, we formulate the motion estimation as a labeling problem. The candidate labels are initialized by adopting a hierarchical block matching method. Then, we employ a graph cut algorithm to efficiently solve the global labeling problem with candidate labels. Experimental results show that the proposed approach can well preserve the motion details and outperforms all other block based motion estimation methods in terms of endpoint error and angle error on the Middleburry optical flow benchmark.

Motion vector fields (MVFs) are able to produce a more accurate prediction image than conventional block based motion compensation. However, MVFs are not used in conventional video coding standards due to the difficulty of efficient estimation and compression. In this work, we propose an MVF based video coding framework. We formulate the estimation of the MVF as a discrete optimization problem by both optimizing the residual energy and MVF smoothness, which can be efficiently solved by a graph cut algorithm with initialized motion vectors for each pixel. We then propose a modified rate distortion optimization approach for the MVF compression. Experimental results show that the proposed method has comparable performance in terms of object quality compared to the state-of-art of HEVC, while it has a better subjective performance by overcoming the block artifacts problem.

Error Diffusion has been widely adopted in the printing industry due to its good visual quality and simple implementation. However, error diffusion still possesses its own deficiencies. Thus multiscale error diffusion (MED) has been developed, and this method outperforms traditional error diffusion according to extensive research results. The majority of previous halftone image watermarking techniques cannot be directly applied to MED halftone images. Since there is no halftone visual watermarking (HVW) method for MED halftone images in existing methods, the authors propose the first HVW method for MED halftone images, Copyright Protecting Multiscale Error Diffusion (CoP-MED), in this paper. By adopting the visual cryptography principle, CoP-MED can effectively embed a secret pattern into two MED halftone images, where the secret pattern can be decoded clearly by simply overlaying the two stego halftone images or performing not-exclusive-or operation between them. Parameter selection is also discussed based on the experimental results. Later, in comparison tests, CoP-MED shows superior performance compared to existing works. Copyright © 2015, IGI Global.

Image colorization aims to add colors to grayscale images, which used to be a time-consuming and tedious task that requires lots of human efforts. In this paper, we present a novel colorization method based on color propagation and rank minimization. Given a small portion of chrominance values and a grayscale image, we firstly propagate the known color values to other pixels to be colorized. As the colorized image after color propagation is not accurate, we then define a confidence matrix to measure the propagation fidelity. Finally, pixels that have propagated chrominance values with confidence are colorized by rank minimization, which exploits the redundancy of natural images. Experimental results on real data set show that our proposed method achieves state-of-the-art colorization quality.

While modern displays offer high dynamic range (HDR) with large bit-depth for each rendered pixel, the bulk of legacy image and video contents were captured using cameras with shallower bit-depth. In this paper, we study the bit-depth enhancement problem for images, so that a high bit-depth (HBD) image can be reconstructed from an input low bit-depth (LBD) image. The key idea is to apply appropriate smoothing given the constraints that reconstructed signal must lie within the per-pixel quantization bins. Specifically, we first define smoothness via a signal-dependent graph Laplacian, so that natural image gradients can nonetheless be interpreted as low frequencies. Given defined smoothness prior and observed LBD image, we then demonstrate that computing the most probable signal via maximum a posteriori (MAP) estimation can lead to large expected distortion. However, we argue that MAP can still be used to efficiently estimate the AC component of the desired HBD signal, which along with a distortion-minimizing DC component, can result in a good approximate solution that minimizes the expected distortion. Experimental results show that our proposed method outperforms existing bit-depth enhancement methods in terms of reconstruction error. © 2014 IEEE.

Images are often decoded with noise at receiver due to capturing errors and/or signal quantization during compression. Further, it is often necessary to display a decoded image at a higher resolution than the captured one, given available high-resolution (HR) display or a need to zoom-in for detailed examination. In this paper, we address the problems of image denoising and super-resolution (SR) jointly in one unified graph-based framework, focusing on a special class of signals called generalized piecewise smooth (GPWS) images. GPWS images are composed mostly of smooth regions connected by transition regions, and represent an important subclass of images, including cartoon, sub-regions of video frames with captions, graphics images in video games, etc. Like our previous work on piecewise smooth (PWS) images, GPWS images also imply simple-enough graph representations in the pixel domain, so that suitable graph-based filtering techniques can be readily applied. Specifically, leveraging on previous work on graph spectral analysis, for a given pixel block in low-resolution (LR) we first use the second eigenvector of a computed graph Laplacian matrix to identify a hard boundary, and then use the third eigenvector to identify two piecewise smooth regions and a transition region that separates them. The LR hard boundary is then super-resolved into HR via a procedure based on local self-similarity, while graph weights of the LR transition region is mapped to those of the HR transition region via polynomial fitting. Using the computed HR boundary and weights in the transition region, we construct a suitable HR graph corresponding to the LR counterpart, and perform joint denoising / SR using a graph smoothness prior. Experimental results show that our proposed algorithm outperforms two representative separable denoising / SR schemes in both subjective and objective quality. © 2014 IEEE.

The emerging cloud gaming technology has been growing fast, driving up huge mobile consumer demands. The video streaming based cloud gaming scenario renders the game scenes in the cloud servers, and streams the encoded sequences to the thin clints where the game scenes are decoded and displayed to the players. However, current existing clouding gaming services have some problems, such as the latency and bandwidth limitation. The size of the video stream is usually quite large which requires heavy transmission. Worse still, the frame data rate will burst when the game scenes contain fast translation or rotation, resulting in strong latency problem. In this paper, we propose a novel video streaming based cloud gaming algorithm which reduces the burst of the frame rate significantly. There are mainly two innovations in this paper. Firstly, based on the analysis of the motion estimation strategy in the video codec, we introduce image homography technique for better motion prediction. Meanwhile, according to the rasterization rules of the game engine, we present a special designed interpolation algorithm named Edge Preserved Interpolation (EPI), for more accurate edge interpolation and further reduce the residues in the edge regions. The proposed algorithm is implemented on the x264 platform. Experimental results show that our algorithm has 18.0% BD-rate reduction compared with x264.

Image denoising is the most basic inverse imaging problem. As an under-determined problem, appropriate definition of image priors to regularize the problem is crucial. Among recent proposed priors for image denoising are: i) graph Laplacian regularizer where a given pixel patch is assumed to be smooth in the graph-signal domain; and ii) self-similarity prior where image patches are assumed to recur throughout a natural image in non-local spatial regions. In our first contribution, we demonstrate that the graph Laplacian regularizer converges to a continuous time functional counterpart, and careful selection of its features can lead to a discriminant signal prior. In our second contribution, we redefine patch self-similarity in terms of patch gradients and argue that the new definition results in a more accurate estimate of the graph Laplacian matrix, and thus better image denoising performance. Experiments show that our designed algorithm based on graph Laplacian regularizer and gradient-based self-similarity can outperform non-local means (NLM) denoising by up to 1.4 dB in PSNR. © 2014 Asia-Pacific Signal and Information Processing Ass.

This work proposes a novel scalable image compression method for stream cipher encrypted images. The bit stream in the base layer is produced by coding a series of non-overlapping patches of the uniformly down-sampled version of the encrypted image. An off-line learning approach can be exploited to model the reconstruction error of original image patch based on the intrinsic relationship between the local complexity and the length of the compressed bit stream. This error model leads to a greedy strategy of adaptively selecting pixels to be coded in the enhancement layer. At the decoder side, an iterative, multi-scale technique is developed to reconstruct the image from available pixel samples. Experimental results demonstrate that the proposed scheme outperforms the state-of-the-art in terms of rate-distortion (RD) performance at low and medium rate regions. © 2014 IEEE.

A common assumption in stereo matching is that the corresponding pixels in stereo images have similar pixel values. Unfortunately, such an assumption may not be true due to radiometric variations in different views, leading to severely degraded matching results. In this letter, we propose a radiometrically invariant stereo matching algorithm called Optimal Local Adaptive Radiometric Compensation (LARAC). In LARAC, we approximate the spatially varying Pixel Value Correspondence Function (PVCF) between a corresponding pixel pair as a locally consistent polynomial within an optimal local adaptive window. The optimal polynomial coefficients are obtained for each candidate disparity value and are used to compute the matching cost. Meanwhile, a self-correction property is achieved by the proposed LARAC, leading to reduced matching errors for the outlier pixels. Experimental results suggest that the proposed LARAC outperforms other state-of-the-art stereo matching algorithms. © 1994-2012 IEEE.

Systems, methods, and apparatus for adaptively zeroing out transform coefficients utilizing a bit-stealing parameter are presented herein. A partitioning component can be configured to separate an image into blocks of video data. Further, a frequency transform component can be configured to transform pixels of a block of the blocks into transform coefficients. Furthermore, a bit-stealing quantization component configured to predefine quantization intervals. Moreover, the bit-stealing quantization component can modify a size of a quantization interval of the quantization intervals based on a variable bit-stealing parameter. Further, the bit-stealing component can quantize a transform coefficient of the transform coefficients, based on the quantization interval, to a quantized coefficient value of quantized coefficient values.

Film grain noise (FGN) is generated by the procedure of capturing pictures using photographic film. Images with FGN are subjectively pleasing. However, FGN is difficult to compress and its pleasant features are difficult to preserve when the images are resized. So in literature, FGN is extracted first, then regenerated for the processed noise-free images. In this paper a new method is proposed to generate FGN. In contrast to some other models which generate FGN in spatial domain, our method captures the statistic feature of FGN in frequency domain. FGN is signal dependent and the signal independent scaled noise image (SNI) is obtained by scaling FGN by corresponding noise-free image. We model each discrete cosine transform (DCT) coefficient of SNI as a Gaussian random variable. The Gaussian model parameters can be estimated from the stack of all the blocks in SNI based on stationary assumption. Experimental results show that proposed model recovers FGN with similar visual and spectrum properties to the original FGN. We also apply proposed model in superresolution and the quality of resultant images are improved.