We propose a method to retrieve similar and duplicate images from a JPEG (Joint Photographic Image Group) image database. Similarity level is decided based on the DCT (Discrete Cosine Transform) coefficients signs. The method is simple and fast because it uses the DCT coefficients signs as features, which can be obtained directly after partial decoding of JPEG bitstream. The method is robust to JPEG compression, in which similarity level of duplicate images, i.e., images that are compressed from the same original images with different compression ratios, is not disguised due to JPEG compression. Simulation results showed the superiority of the method compared to previous methods in terms of computational complexity and robustness to JPEG compression.

In some applications, such as the echo cancellation problem of satellite-linked communication channels, there occurs a problem of estimation of a long impulse response, which consists of a long flat delay and a short dispersive response region. In this paper, it is shown that the use of the adaptive algorithm based on the frequency domain sampling theorem enables efficient identification of the long impulse response. The use of the proposed technique can lead to the reduction of both the number of adaptive weights and the complexity of flat delay estimation.

This letter proposes an efficient compression scheme for the copyright- and privacy-protected image trading system. The proposed scheme multiplies pseudo random signs to amplitude components of discrete cosine transformed coefficients before the inverse transformation is applied. The proposed scheme efficiently compresses amplitude-only image which is the inversely transformed amplitude components, and the scheme simultaneously improves the compression efficiency of phase-only image which is the inversely transformed phase components, in comparison with the conventional systems.

This letter proposes an efficient lossless compression method for high dynamic range (HDR) images in OpenEXR format. The proposed method transforms an HDR image to an indexed image and packs the histogram of the indexed image. Finally the packed image is losslessly compressed by using any existing lossless compression algorithm such as JPEG 2000. Experimental results show that the proposed method reduces the bit rate of compressed OpenEXR images compared with equipped lossless compression methods of OpenEXR format.

In this paper, we propose a 2-layer lossless coding method for high dynamic range (HDR) images based on range compression and adaptive inverse tone-mapping. Recently, HDR images, which have a wider range of luminance than conventional low dynamic range (LDR) ones, have been frequently used in various fields. Since commonly used devices cannot yet display HDR images, 2-layer coding methods that decode not only HDR images but also their LDR versions have been proposed. We have previously proposed a state-of-the-art 2-layer lossless coding method for HDR images that unfortunately has huge HDR file size. Hence, we introduce two ideas to reduce the HDR file size to less than that of the previous method. The proposed method achieves high compression ratio and experiments show that it outperforms the previous method and other conventional methods.

In this work, a new structure of M-channel linear-phase paraunitary filter banks is proposed, where M is even. Our proposed structure can be regarded as a modification of the conventional generalized linear-phase lapped orthogonal transforms (GenLOT) based on the discrete cosine transform (DCT). The main purpose of this work is to overcome the limitation of the conventional DCT-based GenLOT, and improve the performance of the fast implementation. It is shown that our proposed fast GenLOT is superior to that of the conventional technique in terms of the coding gain. This work also provides a recursive initialization design procedure so as to avoid insignificant local-minimum solutions in the non-linear optimization processes. In order to verify the significance of our proposed method, several design examples are given. Furthermore, it is shown that the fast implementation can be used to construct M-band linear-phase orthonormal wavelets with regularity.

This paper proposes an encryption-then-compression (EtC) system-friendly data hiding scheme for images, where an EtC system compresses images after they are encrypted. The EtC system divides an image into non-overlapping blocks and applies four block-based processes independently and randomly to the image for visual encryption of the image. The proposed scheme hides data to a plain, i.e., unencrypted image and the scheme can take hidden data out from the image encrypted by the EtC system. Furthermore, the scheme serves reversible data hiding, so it can perfectly recover the unmarked image from the marked image whereas the scheme once distorts unmarked image for hiding data to the image. The proposed scheme copes with the three of four processes in the EtC system, namely, block permutation, rotation/flipping of blocks, and inverting brightness in blocks, whereas the conventional schemes for the system do not cope with the last one. In addition, these conventional schemes have to identify the encrypted image so that image-dependent side information can be used to extract embedded data and to restore the unmarked image, but the proposed scheme does not need such identification. Moreover, whereas the data hiding process must know the block size of encryption in conventional schemes, the proposed scheme needs no prior knowledge of the block size for encryption. Experimental results show the effectiveness of the proposed scheme.

In this paper, we propose secure dictionary learning based on a random unitary transform for sparse representation. Currently, edge cloud computing is spreading to many application fields including services that use sparse coding. This situation raises many new privacy concerns. Edge cloud computing poses several serious issues for end users, such as unauthorized use and leak of data, and privacy failures. The proposed scheme provides practical MOD and K-SVD dictionary learning algorithms that allow computation on encrypted signals. We prove, theoretically, that the proposal has exactly the same dictionary learning estimation performance as the non-encrypted variant of MOD and K-SVD algorithms. We apply it to secure image modeling based on an image patch model. Finally, we demonstrate its performance on synthetic data and a secure image modeling application for natural images.

A novel data embedding method for high-quality images, e.g., an image with a peak signal-to-noise ratio of better than 60 [dB] is proposed in this paper. The proposed method precisely generates a watermarked image of the desired and high quality for any images. To do this, this method considers the finite word-length of a luminance value of pixels, i.e., both quantization errors and the range limitation of luminance. The proposed method embeds a watermark sequence, modulated by the mechanism of a spread spectrum scheme, into the dc values of an image in the spatial domain. By employing spread spectrum technology as well as embedding a watermark into the dc values, this method guarantees the high image quality and, simultaneously, provides adequate JPEG tolerance.

In this paper, we propose a secure computation of sparse coding and its application to Encryption-then-Compression (EtC) systems. The proposed scheme introduces secure sparse coding that allows computation of an Orthogonal Matching Pursuit (OMP) algorithm in an encrypted domain. We prove theoretically that the proposed method estimates exactly the same sparse representations that the OMP algorithm for non-encrypted computation does. This means that there is no degradation of the sparse representation performance. Furthermore, the proposed method can control the sparsity without decoding the encrypted signals. Next, we propose an EtC system based on the secure sparse coding. The proposed secure EtC system can protect the private information of the original image contents while performing image compression. It provides the same rate-distortion performance as that of sparse coding without encryption, as demonstrated on both synthetic data and natural images.

A method of identifying JPEG 2000 images with different coding parameters, such as code-block sizes, quantization-step sizes, and resolution levels, is presented. It does not produce false-negative matches regardless of different coding parameters (compression rate, code-block size, and discrete wavelet transform (DWT) resolutions levels) or quantization step sizes. This feature is not provided by conventional methods. Moreover, the proposed approach is fast because it uses the number of zero-bit-planes that can be extracted from the JPEG 2000 codestream by only parsing the header information without embedded block coding with optimized truncation (EBCOT) decoding. The experimental results revealed the effectiveness of image identification based on the new method.

We proposed a method for embedding binary data into JPEG bitstreams and extracting embedded data from JPEG bitstreams using the standard JPEG decoder. In the proposed method, we can decode the image from JPEG bitstreams into which the binary data is embedded first using the traditional standard JPEG decoder, and then we can extract the embedded binary data perfectly by the post-processing from the decoded JPEG image. For the post-processing, we use only the decoded image data to extract the embedded binary data. Namely, we do not need any kind of particular parameters, which are used for JPEG decoding, such as quantization table value. Thus, we can use the traditional standard JPEG decoder for the pre-processing of extracting binary data. Furthermore, we address the effect of the calculation bit accuracy of discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) for extracting embedded binary data perfectly as post-processing. Simulations using extracting embedded binary data as post-processing are presented to quantify some performance factors concerned. And we confirmed that the proposed method could be of practical use.

This paper proposes a quantization-based image-quality guaranteed watermarking (IQGW) method using a nonorthogonal discrete wavelet transformation. An IQGW method generates watermarked images of a desired image quality for any image, neither with trial and error nor with image-dependent parameters. To guarantee the image-quality, the proposed method adjusts the energy of the watermark sequence to be embedded based on the relationship between a nonorthogonally transformed domain and the spatial domain for the signal energy. This proposed method extracts the embedded watermark by quantization of watermarked coefficients, no reference image, thus, is required. In addition, it is capable of controlling the objective and subjective image-quality of a watermarked image independently. With features mentioned above, the proposed method is suitable for real-time embedding of Motion JPEG 2000 videos. Moreover, it is able to fuse quantization- and correlation-based watermarking.

A pipelined architecture is proposed for the normalized least mean square (NLMS) adaptive digital filter (ADF). Pipelined implementation of the NLMS has not yet been proposed. The proposed architecture is the first attempt to implement the NLMS ADF in the pipelined fashion. The architecture is based on an equivalent expression of the NLMS derived in this study. It is shown that the proposed architecture achieves a constant and a short critical path without producing output latency. In addition, it retains the advantage of the NLMS, i. e. , that the step size that assures the convergence is determined automatically. Computer simulation results that confirm that the proposed architecture achieves convergence characteristics identical to those of the NLMS.

This letter considers a unified tone mapping operation (TMO) for HDR images. The unified TMO can perform tone mapping for various HDR image formats with a single common operation. The integer TMO which can realize unified tone mapping by converting an input HDR image into an intermediate format is proposed. This method can be executed efficiently with low memory and low performance processor. However, only floating-point HDR image formats have been considered in the method. In other words, a long-integer which is one of the HDR image formats has not been considered in the method. This letter applies the method to a long-integer format, and confirm its performance. The experimental results show the proposed method is effective for an integer format in terms of the resources such as the computational cost and the memory cost.

As three dimensional (3D) discrete wavelet transform (DWT) is widely used for high resolution volumetric data compression, and to further improve the performance of lossless coding, the adaptive directional lifting (ADL) structure based on non-separable 3D DWT with a (5,3) filter is proposed in this paper. The proposed 3D DWT has less lifting steps and better prediction performance compared to the existing separable 3D DWT with fixed filter coefficients. It also has compatibility with the conventional DWT defined by the JPEG2000 international standard. The proposed method shows comparable and better results with the non-separable 3D DWT and separable 3D DWT and it is effective for lossless coding of high resolution volumetric data.

A number of successful tone mapping operators (TMOs) for contrast compression have been proposed due to the need to visualize high dynamic range (HDR) images on low dynamic range devices. This paper proposes a novel inverse tone mapping (TM) operation and a new remapping framework with the operation. Existing inverse TM operations require either the store of some parameters calculated in forward TM, or data-depended operations. The proposed inverse TM operation enables to estimate HDR images from LDR ones mapped by the Reinhard's global operator, not only without keeping any parameters but also without any data-depended calculation. The proposed remapping framework with the inverse operation consists of two TM operations. The first TM operation is carried out by the Reinhard's global operator, and then the generated LDR one is stored. When we want different quality LDR ones, the proposed inverse TM operation is applied to the stored LDR one to generate an HDR one, and the second TM operation is applied to the HDR one to generate an LDR one with desirable quality, by using an arbitrary TMO. This framework allows not only to visualize an HDR image on low dynamic range devices at low computing cost, but also to efficiently store an HDR one as an LDR one. In simulations, it is shown that the proposed inverse TM operation has low computational cost, compared to the conventional ones. Furthermore, it is confirmed that the proposed framework allows to remap the stored LDR one to another LDR one whose quality is the same as that of the LDR one remapped by the conventional inverse TMO with parameters.

In this letter, we propose a new error protection technique for JPEG2000-coded images and also present its evaluation over an OFDM channel. The method exploits the layer structure of the JPEG2000 codestream, a data embedding technique and a forward error correcting code. The main header and data in the top layer are duplicated and protected by the error correcting code. These data are then embedded into the bottom layer for error recovery purposes. Our method offers several features: preserves the same codestream structure as the one in the JPEG2000 part 1 standard, provides multilevel error protection, and can be combined with the existing error resilience technique. Hence, the method accommodates the new requirements for wireless JPEG2000 (JPWL/JPEG2000 part 11).

The main purpose of this paper is to give a new representation method of the convergence characteristics of the LMS algorithm using tap-input vectors. The described representation method is an extended version of the interpretation method based on the orthogonal projection. Using this new representation, we can express the convergence characteristics in terms of tap-input vectors instead of the eigenvalues of the input signal. From this representation, we consider a general method for improving the convergence speed.