Abstract
We present an adaptive lossless video compression algorithm based on predictive coding. The proposed algorithm exploits temporal, spatial, and spectral redundancies in a backward adaptive fashion with extremely low side information. The computational complexity is further reduced by using a caching strategy. We also study the relationship between the operational domain for the coder (wavelet or spatial) and the amount of temporal and spatial redundancy in the sequence being encoded. Experimental results show that the proposed scheme provides significant improvements in compression efficiencies. Index Terms—Integer wavelet transform (IWT), lossless video coding, pixel prediction.
I. INTRODUCTION
DUE to its importance in multimedia applications, most research on video compression has centered on lossy video compression where the focus is on achieving a good tradeoff between the reconstructed quality and the compression ratio. Current lossy video compression standards provide substantial compression efficiency at the cost of minimal degradation of quality. Historically, significantly less interest has been paid to the development of lossless video compression algorithms. Lossless video compression is important to applications in which the video quality cannot tolerate any degradation, such as archiving of a video, compression of medical and satellite videos, etc. Recently, there has been increasing interests in developing lossless video compression techniques [1]–[9]. Memon and Sayood [1] presented a hybrid compression approach, which adaptively utilized spatial, spectral or temporal prediction based on their performance, and employed a 3-D version of the lossless JPEG predictor to incorporate temporal prediction. The authors also investigated an adaptive scheme shifting between the temporal prediction based on block motion compensation and spectral prediction which used the best predictor from one spectral plane on another. In [7], Zhang et al. applied an adaptive combination of a temporal predictor based on block motion compensation and a spatial predictor as de- scribed in CALIC [12]. Brunello et al. [5] introduced a novel temporal prediction technique based on block motion compensation and an optimal 3-D linear prediction algorithm. In their scheme, based on motion information, the pixel to be encoded is predicted by a linear combination of neighboring pixels in the current and reference frames. The optimal linear prediction coefficients are adaptively obtained. Note that the above approaches are based on block motion compensation which requires the transmission of motion vectors, which, in turn, may significantly decrease the compression efficiency. Kyeong et al. proposed a pixel-based prediction scheme [2], in which intraframe or interframe coding is chosen based on temporal or spatial variations. These variations are calculated by using the past reconstructed pixels in the current or reference frame. The encoder selects the mode with less variation. Carotti et al. [4] presented a combination of a MED spatial predictor as described in JPEG-LS [11] and a pixel-based temporal predictor formed from the pixels in the same neighborhood in the previous two frames. In [4], a fixed weighting is used to combine both spatial and temporal prediction. Later, the authors improved the scheme by replacing the fixed weight with an optimal adaptive weight aimed at minimizing the energy of the prediction residual in [3]. Note that [2]–[4] used pixel-based predictors, and the temporal prediction in these schemes did not utilize motion information. Gong et al. [8] proposed a wavelet-based lossless video coding algorithm. Noting that motion compensation in the wavelet domain can be very inefficient for high motion video sequences due to the shift-variant property of the wavelet transform [8], their scheme switches between two operational modes based on the amount of the motion activity between adjacent frames. Differential coding of wavelet coefficients without motion compensation was applied directly to a low motion activity sequence. Otherwise, block motion compensation was first performed in the spatial domain, and then wavelet coefficients of the prediction residuals were entropy coded and transmitted.

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