A crucial deficiency of lossy blockwise image compression is the generation of local artifacts such as ringing defects, obscuration of fine detail, and blocking effect (BE). To date, few published reports of image quality measures (IQMs) have addressed the detection of such errors in a realistic, efficient manner. Exceptions are feature-based IQMs, perceptual IQMs, error detection templates, and quantification of BE that support its reduction in JPEG-and wavelet-compressed imagery. In this paper, we present an enhanced suite of IQMs that emphasize detection of local, feature-specific errors that corrupt visual appearance or numerical integrity of decompressed digital imagery. By the term visual appearance is meant subjective error, in contrast with objectively quantified effects of compression on individual pixel values and their spatial interrelationships. Subjective error is of key importance in human viewing applications, for examples, Internet video. Objective error is primarily of interest in object recognition applications such as automated target recognition (ATR), where implementational concerns involve the effect of compression or decompression algorithms on probability of detection (Pd) and rate of false alarms (Rfa). Analysis of results presented herein emphasizes application- specific quantification of local compression errors. In particular, introduction of extraneous detail (e.g., ringing defects of BE) or obscuration of source detail (e.g., texture masking) adversely impact both subjective and objective error of a decompressed image. Blocking effect is primarily a visual problem, but can confound ATR filters when a target spans a block boundary. Introduction of point or cluster errors primarily degrades ATR filter performance, but can also produce noticeable degradation of fine detail for human visual evaluation of decompressed imagery. In practice, error and performance analysis is supported by examples of ATR imagery including airborne and underwater mono-and multi-spectral images.