We present a lifting-based lapped transform (L-LT) and a reversible symmetric extension (RSE) in the boundary processing for more effective lossy-to-lossless image coding of data with various qualities from only one piece of lossless compressed data. The proposed dual-DCT-lifting-based LT (D2L-LT) parallel processes two identical LTs and consists of 1-D and 2-D DCT-liftings which allow the direct use of a DCT matrix in each lifting coefficient. Since the DCT-lifting can utilize any existing DCT software or hardware, it has great potential for elegant implementations that are dependent on the architecture and DCT algorithm used. In addition, we present an improved RSE (IRSE) that works by recalculating the boundary processing and solves the boundary problem that the DCT-lifting-based L-LT (DL-LT) has. We show that D2L-LT with IRSE mostly outperforms conventional L-LTs in lossy-to-lossless image coding.

Decimation and interpolation methods are utilized in image coding for low bit rate image coding. However, the decimation filter (prefilter) and the interpolation filter (postfilter) are irreversible with each other since the prefilter is a wide matrix (a matrix whose number of columns are larger than that of rows) and the postfilter is a tall one (a matrix whose number of rows are larger than that of columns). There will be some distortions in the reconstructed image even without any compression. The method of interpolation-dependent image downsampling (IDID) was used to tackle the problem of producing optimized downsampling images, which led to the optimized prefilter of a given postfilter. We propose integrating the IDID with time-domain lapped transforms (TDLTs) to improve image coding performance.

In this paper, we present a method to combine lapped transforms with various downsampling factors. The factor is changed depending on a local feature of a given signal, and it can be realized by using time-domain lapped transforms. In image coding application, our method maintains good image coding performance for a wide range of bitrates and fills the gap between undersampled and critically-/oversampled systems.

Acoustic data transmission is a technique which embeds data in a sound wave imperceptibly and detects it at a receiver. The data are embedded in an original audio signal and transmitted through the air by playing back the data-embedded audio using a loudspeaker. At the receiver, the data are extracted from the received audio signal captured by a microphone. In our previous work, we proposed an acoustic data transmission system designed based on phase modification of the modulated complex lapped transform (MCLT) coefficients. In this paper, we propose the spectral magnitude adjustment (SMA) technique which not only enhances the quality of the data-embedded audio signal but also improves the transmission performance of the system.

This paper proposes two algorithms to balance energy consumption among sensor nodes by distributing the workload of image compression tasks within a cluster on wireless sensor networks. The main point of the proposed algorithms is to adopt the energy threshold, which is used when we implement the exchange and/or assignment of tasks among sensor nodes. The threshold is well adaptive to the residual energy of sensor nodes, input image, compressed output, and network parameters. We apply the lapped transform technique, an extended version of the discrete cosine transform, and run length encoding before Lempel-Ziv-Welch coding to the proposed algorithms to improve both quality and compression rate in image compression scheme. We extensively conduct computational experiments to verify the our methods and find that the proposed algorithms achieve not only balancing the total energy consumption among sensor nodes and, thus, increasing the overall network lifetime, but also reducing block noise in image compression.

We propose a moving picture coding by lapped transform and an edge adaptive deblocking filter to reduce the blocking distortion. We apply subband coding (SBC) with lapped transform (LT) and zero pruning set partitioning in hierarchical trees (zpSPIHT) to encode the difference picture. Effective coding using zpSPIHT was achieved by quantizing and pruning the quantized zeros. The blocking distortion caused by block motion compensated prediction is reduced by an edge adaptive deblocking filter. Since the original edges can be detected precisely at the reference picture, an edge adaptive deblocking filter on the predicted picture is very effective. Experimental results show that blocking distortion has been visually reduced at very low bit rate coding and better PSNRs of about 1.0 dB was achieved.

In this paper, we present a design and implementation of the M-channel linear-phase filter banks with unequal-length and same center of symmetry. The filter banks are separated into paraunitary and biorthogonal case. We discuss both cases. A novel filter bank can be regarded as a special class of generalized lapped transform with arbitrary number of channels M. In image coding applications, long basis functions should be used to avoid the blocking artifacts in low-frequency bands, while short basis functions should be used to reduce the ringing artifacts in high-frequency bands. Having the same center of symmetry is suitable for progressive image coder [SPIHT]. Filter banks with such characteristics can be achieved structurally by taking acount of the lattice structure. Finally, several design and image coding examples are shown.

This paper presents the design of a modulated complex lapped transform (MCLT) processor and its complex programmable logic device (CPLD) implementation. The MCLT is a 2x oversampled DFT filter bank; it performs well in applications that require a complex filter bank, such as noise reduction and acoustic echo cancellation. First, we show that the MCLT can be mapped to a Fast Fourier Transform (FFT). Then efficient implementation for fast MCLT computation is realized on the CPLD hardware using pipelining techniques. Detailed circuit design for the MLCT processor is presented, as well as timing diagrams for design verification and performance evaluation.

In this paper, the Lapped Orthogonal Transform (LOT) with unequal length basis function is considered. The proposed unequal length LOT (ULLOT) has both long basis of length 2M and short basis of length M, while the lengths of all bases of the conventional LOT are 2M. A new class of LOT can be constructed with some modifications of Malvar's Fast LOT. Therefore, the fast algorithm for the Discrete Cosine Transform (DCT) will surely facilitate the computation of the ULLOT. Although the computational complexity of the ULLOT is always lower than that of the LOT, there exist some cases where the coding gain of the ULLOT becomes slightly higher than that of the LOT. Its ability to reduce ringing artifacts is an attractive feature as well. The size-limited structure for the finite length signal is investigated and the ULLOTs are tested on image coding application. The simulation results confirm the validity of the proposed ULLOT.

In this paper, a two-dimensional (2-D) binary-valued (BV) lapped transform (LT) is proposed. The proposed LT has basis images which take only BV elements and satisfies the axial-symmetric (AS) property. In one dimension, there is no 2-point LT with the symmetric basis vectors, and the property is achieved only with the non-overlapping basis which the Hadamard transform (HT) has. Hence, in two dimension, there is no 22-point separable ASLT, and only 2-D HT can be the 22-point separable AS orthogonal transform. By taking non-separable BV basis images, this paper shows that a 22-point ASLT can be obtained. Since the proposed LT is similar to HT, it is referred to as the lapped Hadamard transform (LHT). LHT of larger size is shown to be provided with a tree structure. In addition, LHT is shown to be efficiently implemented by a lattice structure.